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Zhao J, Li J, Yao J, Lin G, Chen C, Ye H, He X, Qu S, Chen Y, Wang D, Liang Y, Gao Z, Wu F. Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction. Comput Biol Med 2024; 175:108437. [PMID: 38669732 DOI: 10.1016/j.compbiomed.2024.108437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024]
Abstract
Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.
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Affiliation(s)
- Jungang Zhao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - JiaCheng Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Jiangqiao Yao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Ganglian Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Chao Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Huajun Ye
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Xixi He
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Shanghu Qu
- Department of Urology, Yunnan Tumor Hospital and the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
| | - Yuxin Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Danhong Wang
- Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Yingqi Liang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Fang Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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2
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Mao J, Zhu Z, Xia M, Zhou M, Wang L, Xia J, Wang Z. Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease. Comput Biol Med 2024; 175:108394. [PMID: 38657464 DOI: 10.1016/j.compbiomed.2024.108394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/12/2024] [Accepted: 04/01/2024] [Indexed: 04/26/2024]
Abstract
Gastroesophageal reflux disease (GERD) profoundly compromises the quality of life, with prolonged untreated cases posing a heightened risk of severe complications such as esophageal injury and esophageal carcinoma. The imperative for early diagnosis is paramount in averting progressive pathological developments. This study introduces a wrapper-based feature selection model based on the enhanced Runge Kutta algorithm (SCCRUN) and fuzzy k-nearest neighbors (FKNN) for GERD prediction, named bSCCRUN-FKNN-FS. Runge Kutta algorithm (RUN) is a metaheuristic algorithm designed based on the Runge-Kutta method. However, RUN's effectiveness in local search capabilities is insufficient, and it exhibits insufficient convergence accuracy. To enhance the convergence accuracy of RUN, spiraling communication and collaboration (SCC) is introduced. By facilitating information exchange among population individuals, SCC expands the solution search space, thereby improving convergence accuracy. The optimization capabilities of SCCRUN are experimentally validated through comparisons with classical and state-of-the-art algorithms on the IEEE CEC 2017 benchmark. Subsequently, based on SCCRUN, the bSCCRUN-FKNN-FS model is proposed. During the period from 2019 to 2023, a dataset comprising 179 cases of GERD, including 110 GERD patients and 69 healthy individuals, was collected from Zhejiang Provincial People's Hospital. This dataset was utilized to compare our proposed model against similar algorithms in order to evaluate its performance. Concurrently, it was determined that features such as the internal diameter of the esophageal hiatus during distention, esophagogastric junction diameter during distention, and external diameter of the esophageal hiatus during non-distention play crucial roles in influencing GERD prediction. Experimental findings demonstrate the outstanding performance of the proposed model, with a predictive accuracy reaching as high as 93.824 %. These results underscore the significant advantage of the proposed model in both identifying and predicting GERD patients.
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Affiliation(s)
- Jinlei Mao
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Zhihao Zhu
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Minjun Xia
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Menghui Zhou
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Li Wang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Jianfu Xia
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Zhifei Wang
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Li R, Chen S, Xia J, Zhou H, Shen Q, Li Q, Dong Q. Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model. Comput Biol Med 2024; 175:108447. [PMID: 38691912 DOI: 10.1016/j.compbiomed.2024.108447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/23/2024] [Accepted: 04/07/2024] [Indexed: 05/03/2024]
Abstract
Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. This study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, then combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. The effectiveness of the proposed algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. The experimental results reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. The proposed method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.
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Affiliation(s)
- Rizeng Li
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Sunmeng Chen
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jianfu Xia
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Hong Zhou
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Qingzheng Shen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Qiantong Dong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
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Chen X, Zhao D, Ji H, Chen Y, Li Y, Zuo Z. Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study. Comput Biol Med 2024; 174:108439. [PMID: 38643596 DOI: 10.1016/j.compbiomed.2024.108439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/26/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024]
Abstract
Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA. Within this study, we introduce a wrapper feature selection model denoted as bWFMVO-SVM-FS, which amalgamates the water flow-based multi-verse optimizer (WFMVO) and support vector machine (SVM) technology. Initially, WFMVO is benchmarked against eleven state-of-the-art algorithms, with its efficiency in searching for optimized feature subsets within the model validated on IEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequently, the developed bWFMVO-SVM-FS model is employed to analyze a cohort of 870 consecutively registered cases of neonates and infants with cholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children's Hospital, both affiliated with Shanghai Jiao Tong University. The results underscore the remarkable predictive capacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyl transferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly correlated with early symptoms in BA infants. Furthermore, leveraging these five significant features enhances the interpretability of the machine learning model's performance outcomes for medical professionals, thereby facilitating more effective clinical decision-making.
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Affiliation(s)
- Xuting Chen
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Dongying Zhao
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Haochen Ji
- The Seventh Research Division, Beihang University (BUAA), Beijing, China
| | - Yihuan Chen
- Department of Neonatology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yahui Li
- Department of Neonatology, Xinhua Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Zongyu Zuo
- The Seventh Research Division, Beihang University (BUAA), Beijing, China.
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Qiu F, Heidari AA, Chen Y, Chen H, Liang G. Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data. Sci Rep 2024; 14:8599. [PMID: 38615048 PMCID: PMC11016116 DOI: 10.1038/s41598-024-59064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/06/2024] [Indexed: 04/15/2024] Open
Abstract
Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.
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Affiliation(s)
- Feng Qiu
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yi Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, 325035, China.
| | - Guoxi Liang
- Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China.
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Nie F, Liu M, Zhang P. Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation. Sci Rep 2024; 14:7642. [PMID: 38561478 PMCID: PMC10984966 DOI: 10.1038/s41598-024-58456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/29/2024] [Indexed: 04/04/2024] Open
Abstract
Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.
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Affiliation(s)
- Fangyan Nie
- Computer and Information Engineering College, Guizhou University of Commerce, Guiyang, 550014, China.
| | - Mengzhu Liu
- Computer and Information Engineering College, Guizhou University of Commerce, Guiyang, 550014, China
| | - Pingfeng Zhang
- College of Marxism, Guizhou University of Commerce, Guiyang, 550014, China
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Wei Y, Othman Z, Daud KM, Luo Q, Zhou Y. Advances in Slime Mould Algorithm: A Comprehensive Survey. Biomimetics (Basel) 2024; 9:31. [PMID: 38248605 PMCID: PMC10813181 DOI: 10.3390/biomimetics9010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 01/23/2024] Open
Abstract
The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.
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Affiliation(s)
- Yuanfei Wei
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Xiangsihu College, Guangxi Minzu University, Nanning 530225, China
| | - Zalinda Othman
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Xiangsihu College, Guangxi Minzu University, Nanning 530225, China
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
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