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Jia W, Chen S, Yang L, Liu G, Li C, Cheng Z, Wang G, Yang X. Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer. Comput Biol Med 2024; 175:108440. [PMID: 38701589 DOI: 10.1016/j.compbiomed.2024.108440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 03/20/2024] [Accepted: 04/07/2024] [Indexed: 05/05/2024]
Abstract
The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics-achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.
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Affiliation(s)
- Wenyuan Jia
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China; Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China.
| | - Shu Chen
- Department of Thoracic Surgery, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Lili Yang
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Guomin Liu
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China; Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China.
| | - Chiyu Li
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
| | - Zhiqiang Cheng
- Scientific and Technological Innovation Center of Health Products and Medical Materials with Characteristic Resources of Jilin Province, China; College of Resources and Environment, Jilin Agriculture University, Changchun, 130118, China.
| | - Guoqing Wang
- Zhejiang Suosi Technology Co. Ltd, Wenzhou, 325000, Zhejiang, China.
| | - Xiaoyu Yang
- Department of Orthopedics, The Second Hospital of Jilin University, Changchun, 130041, China.
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Su H, Zhao D, Heidari AA, Cai Z, Chen H, Zhu J. Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats. Basic Clin Pharmacol Toxicol 2024; 134:250-271. [PMID: 37945549 DOI: 10.1111/bcpt.13959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/12/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.
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Affiliation(s)
- Hang Su
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Zhennao Cai
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, China
| | - Jiayin Zhu
- Laboratory Animal Center, Wenzhou Medical University, Wenzhou, China
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Peng L, Cai Z, Heidari AA, Zhang L, Chen H. Hierarchical Harris hawks optimizer for feature selection. J Adv Res 2023; 53:261-278. [PMID: 36690206 PMCID: PMC10658428 DOI: 10.1016/j.jare.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/12/2022] [Accepted: 01/14/2023] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection. OBJECTIVES To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features. METHODS We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets. RESULTS Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms. CONCLUSION Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.
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Affiliation(s)
- Lemin Peng
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China; College of Information Engineering, Yangzhou University, Yangzhou 225127, China; Research and Development Center for E-Learning , Ministry of Education, Beijing 100039, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
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Li X, Lin Z, Lv H, Yu L, Heidari AA, Zhang Y, Chen H, Liang G. Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design. iScience 2023; 26:107736. [PMID: 37810256 PMCID: PMC10558746 DOI: 10.1016/j.isci.2023.107736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/21/2023] [Accepted: 08/23/2023] [Indexed: 10/10/2023] Open
Abstract
The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.
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Affiliation(s)
- Xinru Li
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Zihan Lin
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Haoxuan Lv
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Liang Yu
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
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Wu Q, Tang X, Li R, Liu L, Chen HL. An enhanced decision-making framework for predicting future trends of sharing economy. PLoS One 2023; 18:e0291626. [PMID: 37797038 PMCID: PMC10553323 DOI: 10.1371/journal.pone.0291626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.
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Affiliation(s)
- Qiong Wu
- School of Marxism, Wenzhou University, Wenzhou, China
| | - Xiaoxiao Tang
- School of Marxism, Wenzhou University, Wenzhou, China
| | - Rongjie Li
- Wenzhou Business College, Wenzhou, China
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Hui-Ling Chen
- College of Computer Science an Artificial Intelligence, Wenzhou University, Wenzhou, China
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Zhou W, Wang P, Zhao X, Chen H. Anti-sine-cosine atom search optimization (ASCASO): a novel approach for parameter estimation of PV models. Environ Sci Pollut Res Int 2023; 30:99620-99651. [PMID: 37620698 DOI: 10.1007/s11356-023-28777-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 07/09/2023] [Indexed: 08/26/2023]
Abstract
Nowadays, solar power generation has gradually become a part of electric energy sharing. How to effectively enhance the energy conversion efficiency of solar cells and components has gradually emerged as a focal point of research. This paper presents a boosted atomic search optimization (ASO) with a new anti-sine-cosine mechanism (ASCASO) to realize the parameter estimation of photovoltaic (PV) models. The anti-sine-cosine mechanism is inspired by the update principle of sine cosine algorithm (SCA) and the mutation strategy of linear population size reduction adaptive differential evolution (LSHADE). The working principle of anti-sine-cosine mechanism is to utilize two mutation formulas containing arcsine and arccosine functions to further update the position of atoms. The introduction of anti-sine-cosine mechanism achieves the populations' random handover and promotes the neighbors' information communication. For better evaluation, the proposed ASCASO is devoted to estimate parameters of three PV models of R.T.C France, one Photowat-PWP201 PV module model, and two commercial polycrystalline PV panels including STM6-40/36 and STM6-120/36 with monocrystalline cells. The proposed ASCASO is compared with nine reported comparative algorithms to assess the performance. The results of parameter estimation for different PV models of various methods demonstrate that ASCASO performs more accurately and reliably than other reported comparative methods. Thus, ASCASO can be considered a highly effective approach for accurately estimating the parameters of PV models.
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Affiliation(s)
- Wei Zhou
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China
| | - Pengjun Wang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China.
| | - Xuehua Zhao
- School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China
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Li Y, Fu Y, Liu Y, Zhao D, Liu L, Bourouis S, Algarni AD, Zhong C, Wu P. An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension. Comput Biol Med 2023; 164:107293. [PMID: 37591162 DOI: 10.1016/j.compbiomed.2023.107293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/25/2023] [Accepted: 07/28/2023] [Indexed: 08/19/2023]
Abstract
Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China.
| | - Yujie Fu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| | - Yining Liu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin 130032, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif 21944, Saudi Arabia.
| | - Abeer D Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Chuyue Zhong
- The First Clinical College, Wenzhou Medical University, Wenzhou 325000, China.
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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Zhang X, Lu B, Zhang L, Pan Z, Liao M, Shen H, Zhang L, Liu L, Li Z, Hu Y, Gao Z. An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction. Comput Biol Med 2023; 163:107166. [PMID: 37364530 DOI: 10.1016/j.compbiomed.2023.107166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023]
Abstract
Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.
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Affiliation(s)
- Xiang Zhang
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Bin Lu
- Wenzhou City Bureau of Justice, Wenzhou, Zhejiang, 325000, China.
| | - Lyuzheng Zhang
- B-soft Co.,Ltd., B-soft Wisdom Building, No.92 Yueda Lane, Binjiang District, Hangzhou, 310052, China.
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Minjie Liao
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Huihui Shen
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| | - Li Zhang
- Wenzhou Hongsheng Intellectual Property Agency (General Partnership), Wenzhou, Zhejiang, 325000, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Zuxiang Li
- Organization Department of the Party Committee, Wenzhou University, Wenzhou, 325000, China.
| | - YiPao Hu
- Wenzhou Health Commission, Wenzhou, Zhejiang, 325000, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Chen Z, Xinxian L, Guo R, Zhang L, Dhahbi S, Bourouis S, Liu L, Wang X. Dispersed differential hunger games search for high dimensional gene data feature selection. Comput Biol Med 2023; 163:107197. [PMID: 37390761 DOI: 10.1016/j.compbiomed.2023.107197] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/02/2023]
Abstract
The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.
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Affiliation(s)
- Zhiqing Chen
- School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, 325035, China.
| | - Li Xinxian
- Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China.
| | - Ran Guo
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China; College of Information Engineering, Yangzhou University, Yangzhou, 225127, China; Research and Development Center for E-Learning, Ministry of Education, Beijing, 100039, China.
| | - Sami Dhahbi
- Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil, Aseer, 62529, Saudi Arabia.
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Xianchuan Wang
- Information Technology Center, Wenzhou Medical University, Wenzhou, 325035, China.
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10
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Lu H, Huang L, Xie Y, Zhou Z, Cui H, Jing S, Yang Z, Zhu D, Wang S, Bao D, Liang G, Cai Z, Chen H, He W. Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine. Heliyon 2023; 9:e18832. [PMID: 37588610 PMCID: PMC10425907 DOI: 10.1016/j.heliyon.2023.e18832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.
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Affiliation(s)
- Haoxuan Lu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Li Huang
- Department of Emergency, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Yanqing Xie
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhong Zhou
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Hanbin Cui
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Sheng Jing
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhuo Yang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Decai Zhu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Shiqi Wang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Donggang Bao
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Wenming He
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
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11
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Chen J, Cai Z, Heidari AA, Chen H, He Q, Escorcia-Gutierrez J, Mansour RF. Multi-threshold image segmentation based on an improved differential evolution: Case study of thyroid papillary carcinoma. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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12
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Chen Z, Xuan P, Heidari AA, Liu L, Wu C, Chen H, Escorcia-Gutierrez J, Mansour RF. An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection. iScience 2023; 26:106679. [PMID: 37216098 PMCID: PMC10193239 DOI: 10.1016/j.isci.2023.106679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/01/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
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Affiliation(s)
- Zhiqing Chen
- School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Ali Asghar Heidari
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
| | - Chengwen Wu
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla 080002, Colombia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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13
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Chen J, Cai Z, Chen H, Chen X, Escorcia-Gutierrez J, Mansour RF, Ragab M. Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing. J Bionic Eng 2023; 20:1-36. [PMID: 37361683 PMCID: PMC10154766 DOI: 10.1007/s42235-023-00365-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 06/28/2023]
Abstract
Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
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Affiliation(s)
- Jiaochen Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Zhennao Cai
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Xiaowei Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, 080002 Barranquilla, Colombia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, 72511, El-Kharga, Egypt
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
- Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo, 11884 Egypt
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14
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Zhang M, Wu Q, Chen H, Heidari AA, Cai Z, Li J, Md Abdelrahim E, Mansour RF. Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction. Biomed Signal Process Control 2023; 83:104638. [PMID: 36741073 PMCID: PMC9889265 DOI: 10.1016/j.bspc.2023.104638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/01/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.
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Affiliation(s)
- Meilin Zhang
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Qianxi Wu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Ali Asghar Heidari
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Zhennao Cai
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China
| | - Jiaren Li
- Wenzhou People's Hospital, Wenzhou, Zhejiang 325099, China
| | - Elsaid Md Abdelrahim
- Faculty of Science, Northern Border University, Arar, Saudi Arabia.,Faculty of Science, Tanta University, Tanta, Egypt
| | - Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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15
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Zhao D, Qi A, Yu F, Heidari AA, Chen H, Li Y. Multi-strategy ant colony optimization for multi-level image segmentation: Case study of melanoma. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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16
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Qiao Z, Li L, Zhao X, Liu L, Zhang Q, Hechmi S, Atri M, Li X. An enhanced Runge Kutta boosted machine learning framework for medical diagnosis. Comput Biol Med 2023; 160:106949. [PMID: 37159961 DOI: 10.1016/j.compbiomed.2023.106949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/27/2023] [Accepted: 04/15/2023] [Indexed: 05/11/2023]
Abstract
With the development and maturity of machine learning methods, medical diagnosis aided with machine learning methods has become a popular method to assist doctors in diagnosing and treating patients. However, machine learning methods are greatly affected by their hyperparameters, for instance, the kernel parameter in kernel extreme learning machine (KELM) and the learning rate in residual neural networks (ResNet). If the hyperparameters are appropriately set, the performance of the classifier can be significantly improved. To boost the performance of the machine learning methods, this paper proposes to improve the Runge Kutta optimizer (RUN) to adaptively adjust the hyperparameters of the machine learning methods for medical diagnosis purposes. Although RUN has a solid mathematical theoretical foundation, there are still some performance defects when dealing with complex optimization problems. To remedy these defects, this paper proposes a new enhanced RUN method with a grey wolf mechanism and an orthogonal learning mechanism called GORUN. The superior performance of the GORUN was validated against other well-established optimizers on IEEE CEC 2017 benchmark functions. Then, the proposed GORUN is employed to optimize the machine learning models, including the KELM and ResNet, to construct robust models for medical diagnosis. The performance of the proposed machine learning framework was validated on several medical data sets, and the experimental results have demonstrated its superiority.
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Affiliation(s)
- Zenglin Qiao
- School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Lynn Li
- China Telecom Stocks Co.,Ltd., Hangzhou Branch, Hangzhou, 310000, China.
| | - Xinchao Zhao
- School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Qian Zhang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China.
| | - Shili Hechmi
- Dept. Computer Sciences, Tabuk University, Tabuk, Saudi Arabia.
| | - Mohamed Atri
- College of Computer Science, King Khalid University, Abha, Saudi Arabia.
| | - Xiaohua Li
- Library, Wenzhou University, Wenzhou, Zhejiang, 325035, China.
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17
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Yang S, Lou L, Wang W, Li J, Jin X, Wang S, Cai J, Kuang F, Liu L, Hadjouni M, Elmannai H, Cai C. Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine. Comput Biol Med 2023; 161:106948. [PMID: 37207406 DOI: 10.1016/j.compbiomed.2023.106948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/04/2023] [Accepted: 04/15/2023] [Indexed: 05/21/2023]
Abstract
Although PNLB is generally considered safe, it is still invasive and risky. Pneumothorax, the most common complication of lung puncture, can cause shortness of breath, chest pain, and even life-threatening. Therefore, the auxiliary diagnosis for pneumothorax is of great clinical interest. This paper proposes an ant colony optimizer with slime mould foraging behavior and collaborative hunting, called SCACO, in which slime mould foraging behavior is combined to improve the convergence accuracy and solution quality of ACOR. Then the ability of ACO to jump out of the local optimum is optimized by an adaptive collaborative hunting strategy when trapped in the local optimum. As a first step toward Pneumothorax diagnostic prediction, we suggested an SVM classifier based on bSCACO (bSCACO-SVM), which uses the proposed SCACO's binary version as the basis for its feature selection algorithms. To demonstrate the SCACO performance, we first used the slime mould foraging behavior and adaptive cooperative hunting strategy, then compared SCACO with nine basic algorithms and nine variants, respectively. Finally, we verified bSCACO-SVM on various widely used public datasets and applied it to the Pneumothorax prediction issue, showing that it has robust classification prediction capacity and can be successfully employed for tuberculous pleural effusion diagnostic prediction.
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Affiliation(s)
- Song Yang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Lejing Lou
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Wangjia Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Jie Li
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xiao Jin
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Shijia Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
| | - Jihao Cai
- Wenzhou Medical University Renji College, Wenzhou, China.
| | - Fangjun Kuang
- School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Myriam Hadjouni
- Department of Computer Sciences, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Chang Cai
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
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18
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Shi M, Chen C, Liu L, Kuang F, Zhao D, Chen X. A grade-based search adaptive random slime mould optimizer for lupus nephritis image segmentation. Comput Biol Med 2023; 160:106950. [PMID: 37120988 DOI: 10.1016/j.compbiomed.2023.106950] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/04/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
The segmentation of medical images is a crucial and demanding step in medical image processing that offers a solid foundation for subsequent extraction and analysis of medical image data. Although multi-threshold image segmentation is the most used and specialized basic image segmentation technique, it is computationally demanding and often produces subpar segmentation results, hence restricting its application. To solve this issue, this work develops a multi-strategy-driven slime mould algorithm (RWGSMA) for multi-threshold image segmentation. Specifically, the random spare strategy, the double adaptive weigh strategy, and the grade-based search strategy are used to improve the performance of SMA, resulting in an enhanced SMA version. The random spare strategy is mainly used to accelerate the convergence rate of the algorithm. To prevent SMA from falling towards the local optimum, the double adaptive weights are also applied. The grade-based search approach has also been developed to boost convergence performance. This study evaluates the efficacy of RWGSMA from many viewpoints using 30 test suites from IEEE CEC2017 to effectively demonstrate the importance of these techniques in RWGSMA. In addition, numerous typical images were used to show RWGSMA's segmentation performance. Using the multi-threshold segmentation approach with 2D Kapur's entropy as the RWGSMA fitness function, the suggested algorithm was then used to segment instances of lupus nephritis. The experimental findings demonstrate that the suggested RWGSMA beats numerous similar rivals, suggesting that it has a great deal of promise for segmenting histopathological images.
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Affiliation(s)
- Manrong Shi
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Chi Chen
- Wenzhou University of Technology, Wenzhou, 325035, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fangjun Kuang
- School of Information engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Xiaowei Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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19
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Hou L, Li R, Mafarja M, Heidari AA, Liu L, Jin C, Zhou S, Chen H, Cai Z, Li C. Image segmentation of Intracerebral hemorrhage patients based on enhanced hunger Games search Optimizer. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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20
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Zhao X, Liu L, Heidari AA, Chen Y, Ma BJ, Chen H, Quan S. An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation. Front Neuroinform 2023; 17:1126783. [PMID: 37006638 PMCID: PMC10064065 DOI: 10.3389/fninf.2023.1126783] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/16/2023] [Indexed: 03/19/2023] Open
Abstract
The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.
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Affiliation(s)
- Xiuzhi Zhao
- College of Artificial Intelligence, Zhejiang Industry & Trade Vocational College, Wenzhou, Zhejiang, China
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
- *Correspondence: Lei Liu,
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China
| | - Benedict Jun Ma
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China
- Huiling Chen,
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, China
- Shichao Quan,
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21
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Zhou X, Cai X, Zhang H, Zhang Z, Jin T, Chen H, Deng W. Multi-strategy competitive-cooperative co-evolutionary algorithm and its application. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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22
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Gu M, Zhang Y, Wen Y, Ai G, Zhang H, Wang P, Wang G. A lightweight convolutional neural network hardware implementation for wearable heart rate anomaly detection. Comput Biol Med 2023; 155:106623. [PMID: 36809696 DOI: 10.1016/j.compbiomed.2023.106623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/09/2023] [Accepted: 01/28/2023] [Indexed: 02/10/2023]
Abstract
In this article, we propose a lightweight and competitively accurate heart rhythm abnormality classification model based on classical convolutional neural networks in deep neural networks and hardware acceleration techniques to address the shortcomings of existing wearable devices for ECG detection. The proposed approach to build a high-performance ECG rhythm abnormality monitoring coprocessor achieves a high degree of data reuse in time and space, which reduces the number of data flows, provides a more efficient hardware implementation and reduces hardware resource consumption than most existing models. The designed hardware circuit relies on 16-bit floating-point numbers for data inference at the convolutional, pooling, and fully connected layers, and implements acceleration of the computational subsystem through a 21-group floating-point multiplicative-additive computational array and an adder tree. The front- and back-end design of the chip was completed on the TSMC 65 nm process. The device has an area of 0.191 mm2, a core voltage of 1 V, an operating frequency of 20 MHz, a power consumption of 1.1419 mW, and requires 5.12 kByte of storage space. The architecture was evaluated using the MIT-BIH arrhythmia database dataset, which showed a classification accuracy of 97.69% and a classification time of 0.3 ms for a single heartbeat. The hardware architecture offers high accuracy with a simple structure, low resource footprint, and the ability to operate on edge devices with relatively low hardware configurations.
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Affiliation(s)
- Minghong Gu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| | - Yuejun Zhang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| | - Yongzhong Wen
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| | - Guangpeng Ai
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| | - Huihong Zhang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
| | - Pengjun Wang
- Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, Zhejiang, China.
| | - Guoqing Wang
- Zhejiang Suosi Technology Co. Ltd, Wenzhou, 325000, Zhejiang, China.
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23
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Liu L, Wang X, Guan M, Fan Y, Yang Z, Li D, Bai Y, Li H. A mixed reality-based navigation method for dental implant navigation method: A pilot study. Comput Biol Med 2023; 154:106568. [PMID: 36739818 DOI: 10.1016/j.compbiomed.2023.106568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/28/2022] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
This in vitro study aimed to put forward the development and investigation of a novel Mixed Reality (MR)-based dental implant navigation method and evaluate implant accuracy. Data were collected using 3D-cone beam computed tomography. The MR-based navigation system included a Hololens headset, an NDI (Northern Digital Inc.) Polaris optical tracking system, and a computer. A software system was developed. Resin models of dentition defects were created for a randomized comparison study with the MR-based navigation implantation system (MR group, n = 25) and the conventional free-hand approach (FH group, n = 25). Implant surgery on the models was completed by an oral surgeon. The precision and feasibility of the MR-based navigation method in dental implant surgery were assessed and evaluated by calculating the entry deviation, middle deviation, apex deviation, and angular deviation values of the implant. The system, including both the hardware and software, for the MR-based dental implant navigation method were successfully developed and a workflow of the method was established. Three-Dimensional (3D) reconstruction and visualization of the surgical instruments, dentition, and jawbone were achieved. Real-time tracking of implant tools and jaw model, holographic display via the MR headset, surgical guidance, and visualization of the intraoperative implant trajectory deviation from the planned trajectory were captured by our system. The MR-based navigation system was with better precise than the free-hand approach for entry deviation (MR: 0.6914 ± 0.2507 mm, FH: 1.571 ± 0.5004 mm, P = 0.000), middle deviation (MR: 0.7156 ± 0.2127 mm, FH: 1.170 ± 0.3448 mm, P = 0.000), apex deviation (MR: 0.7869 ± 0.2298 mm, FH: 0.9190 ± 0.3319 mm, P = 0.1082), and angular deviation (MR: 1.849 ± 0.6120°, FH: 4.933 ± 1.650°, P = 0.000).
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Affiliation(s)
- Lin Liu
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Xiaoyu Wang
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China; Department of Stomatology, PLA Strategic Support Force Special Medical Center, Beijing, 100101, China
| | - Miaosheng Guan
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China; PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Yiping Fan
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Zhongliang Yang
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China
| | - Deyu Li
- Beijing Visual 3D Medical Science and Technology Development Co., LTD., Beijing, 100000, China.
| | - Yuming Bai
- Beijing Visual 3D Medical Science and Technology Development Co., LTD., Beijing, 100000, China
| | - Hongbo Li
- Department of Stomatology, The First Medical Center of PLA General Hospital, Beijing, 100853, China.
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Wang Y, Fan J, Tong Y, Wang L, Wang L, Weng C, Lai C, Song J, Zhang W. Bioinformatics analysis of ferroptosis-related gene AKR1C3 as a potential biomarker of asthma and its identification in BEAS-2B cells. Comput Biol Med 2023; 158:106740. [PMID: 36996663 DOI: 10.1016/j.compbiomed.2023.106740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/24/2023] [Accepted: 03/02/2023] [Indexed: 03/17/2023]
Abstract
Ferroptosis is a newly discovered type of cell death and has recently been shown to be associated with asthma. However, the relationship between them at the genetic level has not been elucidated via informatics analysis. In this study, bioinformatics analyses are conducted using asthma and ferroptosis datasets to identify candidate ferroptosis-related genes using the R software. Weighted gene co-expression network analysis is performed to identify co-expressed genes. Protein-protein interaction networks, the Kyoto encyclopedia of genes and genomes, and gene ontology enrichment analysis are used to identify the potential functions of the candidate genes. We experimentally validate the results of our analysis using small interfering RNAs and plasmids to silence and upregulate the expression of the candidate gene in human bronchial epithelial cells (BEAS-2B). The ferroptosis signature levels are examined. Bioinformatics analysis of the asthma dataset GDS4896 shows that the level of the aldo-keto reductase family 1 member C3 (AKR1C3) gene in the peripheral blood of patients with severe therapy-resistant asthma and controlled persistent mild asthma (MA) is significantly upregulated. The AUC values for asthma diagnosis and MA are 0.823 and 0.915, respectively. The diagnostic value of AKR1C3 is verified using the GSE64913 dataset. The gene module of AKR1C3 is evident in MA and functions through redox reactions and metabolic processes. Ferroptosis indicators are downregulated by the overexpression of AKR1C3 and upregulated by silencing AKR1C3. The ferroptosis-related gene AKR1C3 can be used as a diagnostic biomarker for asthma, particularly for MA, and regulates ferroptosis in BEAS-2B cells.
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Affiliation(s)
- Yufei Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Junwen Fan
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Yu Tong
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lei Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lingya Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Cuiye Weng
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Department of Neonatology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Chuqiao Lai
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Jingjing Song
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
| | - Weixi Zhang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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25
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Houssein EH, Sayed A. A modified weighted mean of vectors optimizer for Chronic Kidney disease classification. Comput Biol Med 2023; 155:106691. [PMID: 36805229 DOI: 10.1016/j.compbiomed.2023.106691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/26/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023]
Abstract
Chronic kidney Disease (CKD), also known as chronic renal disease, is an illness that affects the majority of adults and is defined by a progressive decrease in kidney function over time, particularly in those with diabetes and high blood pressure. Metaheuristic (MH) algorithms based machine learning classifiers have become reliable for medical treatment. The weIghted meaN oF vectOrs (INFO) is a recently developed MH but suffers from a fall into local optimal and slow convergence speed. Therefore, to improve INFO, a modified INFO (mINFO) with two enhancement strategies has been developed. The developed variant utilizes the Opposition-Based Learning (OBL) to improve the local search ability to avoid trapping into the local optimum, and the Dynamic Candidate Solution (DCS) is used to overcome the premature convergence problem in INFO and achieve the appropriate balance between exploration and exploitation ability. The performance of the proposed mINFO based on the k-Nearest Neighbor (kNN) classifier is evaluated on the complex CEC'22 test suite and applied to predict Chronic Kidney Disease (CKD) on datasets extracted from UCI. The statistical results revealed the superiority of mINFO compared with several well-known MH algorithms, including the Harris Hawks Optimization (HHO), the Hunger Games Search (HGS) algorithm, the Moth-Flame Optimization (MFO) algorithm, the Whale Optimization Algorithm (WOA), the Sine Cosine Algorithm (SCA), the Gradient-Based Optimizer (GBO), and the original INFO algorithm. According to our knowledge, this paper is the first of its sort to try employing the proposed mINFO for solving the CEC'22 test suite. Furthermore, the experimental results of mINFO-kNN for classifying two CKD datasets demonstrated its superiority with an overall classification accuracy of 93.17% on two CKD datasets over other competitors.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Awny Sayed
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Chen K, Pan Y, Xiang X, Meng X, Yao D, Lin L, Li X, Wang Y. The nonalcoholic fatty liver risk in prediction of unfavorable outcome after stroke: A nationwide registry analysis. Comput Biol Med 2023; 157:106692. [PMID: 36924734 DOI: 10.1016/j.compbiomed.2023.106692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/31/2023] [Accepted: 02/14/2023] [Indexed: 03/05/2023]
Abstract
Few researches have looked at the relationship between nonalcoholic fatty liver disease (NAFLD) at the time of admission and the long-term outcomes of patients suffering from acute ischemic stroke (AIS). We aimed to probe the relationship between NAFLD risk evaluated by NAFLD indices and long-term endpoints, along with the prognostic value of merging NAFLD indices with established risk markers for the prognosis of AIS patients. The fatty liver index (FLI) and the Hepatic steatosis index (HSI) were used to evaluate NAFLD risk in the Third China National Stroke Registry (CNSR-III), a large, prospective, national, multicenter cohort registry study. NAFLD was defined as FLI ≥35 for males and FLI ≥ 20 for females, as well as HSI>36. Death or major disability (modified Rankin Scale score ≥3) were the primary outcomes following the beginning of a stroke. On patient outcomes, the prognostic performance of two objective NAFLD parameters was evaluated. NAFLD was detected in 32.10-51.90% of AIS patients. After 1-year, 14.5% of the participants had died or suffered a severe outcome. After controlling for known risk factors, NAFLD was associated with a modest probability of adverse outcome (odds ratio,0.72[95% CI, 0.61-0.86] for FLI; odds ratio,0.68[95% CI, 0.55-0.85] for HSI). The inclusion of the two NAFLD indicators in the conventional prediction model was justified by the integrated discrimination index, continuing to increase the model's overall predictive value for long-term adverse outcomes. NAFLD risk was linked to a lower risk of long-term death or major disability in people with AIS. The predictive value of objective NAFLD after AIS was demonstrated in our study.
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Affiliation(s)
- Keyang Chen
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China; School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Research Units of Clinical Translation of Cell Growth Factors and Diseases Research, Chinese Academy of Medical Science, Wenzhou Medical University, Wenzhou, China
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xianglong Xiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Dongxiao Yao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Li Lin
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China; Research Units of Clinical Translation of Cell Growth Factors and Diseases Research, Chinese Academy of Medical Science, Wenzhou Medical University, Wenzhou, China
| | - Xiaokun Li
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China; Research Units of Clinical Translation of Cell Growth Factors and Diseases Research, Chinese Academy of Medical Science, Wenzhou Medical University, Wenzhou, China.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China; Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, China; Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, 2019RU018, China.
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27
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Cui H, Chen G, Guan Y, Zhao H. Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU. Sensors (Basel) 2023; 23:s23052461. [PMID: 36904664 PMCID: PMC10007246 DOI: 10.3390/s23052461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/09/2023] [Accepted: 02/20/2023] [Indexed: 05/14/2023]
Abstract
Due to aerodynamic resistance, aerodynamic noise, and other problems, the further development of traditional high-speed electric multiple units (EMUs) on the open line has been seriously restricted, and the construction of a vacuum pipeline high-speed train system has become a new solution. In this paper, the Improved Detached Eddy Simulation (IDDES) is used to analyze the turbulent characteristics of the near wake region of EMU in vacuum pipes, so as to establish the important relationship between the turbulent boundary layer, wake, and aerodynamic drag energy consumption. The results show that there is a strong vortex in the wake near the tail, which is concentrated at the lower end of the nose near the ground and falls off from the tail. In the process of downstream propagation, it shows symmetrical distribution and develops laterally on both sides. The vortex structure far from the tail car is increasing gradually, but the strength of the vortex is decreasing gradually from the speed characterization. This study can provide guidance for the aerodynamic shape optimization design of the rear of the vacuum EMU train in the future and provide certain reference significance for improving the comfort of passengers and saving the energy consumption caused by the speed increase and length of the train.
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Affiliation(s)
- Hongjiang Cui
- School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
| | - Guanxin Chen
- School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
| | - Ying Guan
- School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China
- Correspondence: (Y.G.); (H.Z.)
| | - Huimin Zhao
- School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
- Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China
- Correspondence: (Y.G.); (H.Z.)
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28
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Zhong C, Li G, Meng Z, Li H, He W. A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput Biol Med 2023; 153:106520. [PMID: 36608463 DOI: 10.1016/j.compbiomed.2022.106520] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/28/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.
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Affiliation(s)
- Changting Zhong
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China.
| | - Gang Li
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; Ningbo Institute of Dalian University of Technology, Ningbo, 315000, China.
| | - Zeng Meng
- School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Haijiang Li
- BIM for Smart Engineering Centre, Cardiff School of Engineering, Cardiff University, Queen's Buildings, Cardiff, CF24 3AA, Whales, UK.
| | - Wanxin He
- Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China.
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29
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Su H, Zhao D, Asghar Heidari A, Liu L, Zhang X, Mafarja M, Chen H. RIME: A physics-based optimization. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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30
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Li Y, Zhao D, Xu Z, Heidari AA, Chen H, Jiang X, Liu Z, Wang M, Zhou Q, Xu S. bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease. Front Neuroinform 2023; 16:1063048. [PMID: 36726405 PMCID: PMC9884708 DOI: 10.3389/fninf.2022.1063048] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/05/2022] [Indexed: 01/18/2023] Open
Abstract
Introduction Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. Methods This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. Results To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. Discussion The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China,*Correspondence: Dong Zhao,
| | - Zhangze Xu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China,Huiling Chen,
| | - Xinyu Jiang
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China,School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Zhifang Liu
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China,School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Mengmeng Wang
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China,School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Qiongyan Zhou
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China
| | - Suling Xu
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China,Suling Xu,
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31
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Li C, Hou L, Pan J, Chen H, Cai X, Liang G. Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine. Front Neuroinform 2022; 16:1078685. [PMID: 36601381 PMCID: PMC9806141 DOI: 10.3389/fninf.2022.1078685] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Although tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity. Methods In this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM. Results To test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets. Discussion In the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.
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Affiliation(s)
- Chengye Li
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingxian Hou
- Department of Rehabilitation, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, China
| | - Jingye Pan
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, Zhejiang, China,Collaborative Innovation Center for Intelligence Medical Education, Wenzhou, Zhejiang, China,Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, Zhejiang, China,Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, China,*Correspondence: Huiling Chen,
| | - Xueding Cai
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China,Xueding Cai,
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, China
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32
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Su H, Han Z, Fu Y, Zhao D, Yu F, Heidari AA, Zhang Y, Shou Y, Wu P, Chen H, Chen Y. Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques. Front Neuroinform 2022; 16:1029690. [PMID: 36590906 PMCID: PMC9800512 DOI: 10.3389/fninf.2022.1029690] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients. Methods Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed. Results To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital. Discussion The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.
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Affiliation(s)
- Hang Su
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China
| | - Zhengyuan Han
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yujie Fu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China,*Correspondence: Dong Zhao,
| | - Fanhua Yu
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yu Zhang
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, China
| | - Yeqi Shou
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, China,Huiling Chen,
| | - Yanfan Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China,Yanfan Chen,
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33
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Liu L, Kuang F, Li L, Xu S, Liang Y. An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer. Comput Biol Med 2022; 151:106227. [PMID: 36368112 DOI: 10.1016/j.compbiomed.2022.106227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/06/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.
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Affiliation(s)
- Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fangjun Kuang
- School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Lingzhi Li
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, 315020, China.
| | - Suling Xu
- Department of Dermatology, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, 315020, China.
| | - Yingqi Liang
- Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
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34
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Pan X, Zhang G, Lin A, Guan X, Chen P, Ge Y, Chen X. An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm. Comput Biol Med 2022; 151:106229. [PMID: 36308897 DOI: 10.1016/j.compbiomed.2022.106229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/08/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%.
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Affiliation(s)
- Xiaotian Pan
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China.
| | - Guodao Zhang
- School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Aiju Lin
- College of international Education, Wenzhou University, Wenzhou 325035, China.
| | - Xiaochun Guan
- Department of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
| | - PingKuo Chen
- Great Bay University, Dongguan City 523000, China.
| | - Yisu Ge
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325100, China.
| | - Xin Chen
- Orthopedics Department of The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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35
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Yang X, Ye X, Zhao D, Heidari AA, Xu Z, Chen H, Li Y. Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization. Front Neuroinform 2022; 16:1041799. [PMID: 36387585 PMCID: PMC9663822 DOI: 10.3389/fninf.2022.1041799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/10/2022] [Indexed: 11/07/2022] Open
Abstract
Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.
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Affiliation(s)
- Xiao Yang
- School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Xiaojia Ye
- School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China
- *Correspondence: Xiaojia Ye,
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
- Dong Zhao,
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Zhangze Xu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Yangyang Li
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Yangyang Li,
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36
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Li Y, Zhao D, Liu G, Liu Y, Bano Y, Ibrohimov A, Chen H, Wu C, Chen X. Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine. Front Neuroinform 2022; 16:956423. [PMID: 36387587 PMCID: PMC9659657 DOI: 10.3389/fninf.2022.956423] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/28/2022] [Indexed: 09/19/2023] Open
Abstract
Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.
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Affiliation(s)
- Yupeng Li
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Guangjie Liu
- College of Computer Science and Technology, Changchun Normal University, Changchun, China
| | - Yi Liu
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yasmeen Bano
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Alisherjon Ibrohimov
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Chengwen Wu
- College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China
| | - Xumin Chen
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou University, Wenzhou, China
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