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Sheng J, Lam S, Zhang J, Zhang Y, Cai J. Multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy. Comput Biol Med 2024; 168:107684. [PMID: 38039891 DOI: 10.1016/j.compbiomed.2023.107684] [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: 08/07/2023] [Revised: 10/06/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023]
Abstract
Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.
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Affiliation(s)
- Jiabao Sheng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - SaiKit Lam
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China.
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2
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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: 1.0] [Reference Citation Analysis] [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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Xia J, Wang Z, Yang D, Li R, Liang G, Chen H, Heidari AA, Turabieh H, Mafarja M, Pan Z. Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis. Comput Biol Med 2022; 143:105206. [PMID: 35101730 DOI: 10.1016/j.compbiomed.2021.105206] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/27/2021] [Accepted: 12/30/2021] [Indexed: 12/13/2022]
Abstract
Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.
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Affiliation(s)
- Jianfu Xia
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Zhifei Wang
- Department of Hepatobiliary, Pancreatic and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China.
| | - Daqing Yang
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Rizeng Li
- Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China.
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Hamza Turabieh
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Taif, Saudi Arabia.
| | - Majdi Mafarja
- Department of Computer Science, Birzeit University, Birzeit, 72439, Palestine.
| | - Zhifang Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, PR China.
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Wang CH, Chen JZ. Combining hidden Markov models with probabilistic Bayes networks to conduct business forecasting and risk simulation. Soft comput 2021. [DOI: 10.1007/s00500-021-05784-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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5
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Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis. Comput Biol Med 2021; 135:104582. [PMID: 34214940 DOI: 10.1016/j.compbiomed.2021.104582] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/13/2021] [Accepted: 06/13/2021] [Indexed: 02/05/2023]
Abstract
Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.
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Zhang Y, Liu R, Heidari AA, Wang X, Chen Y, Wang M, Chen H. Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.038] [Citation(s) in RCA: 130] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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7
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Ye H, Wu P, Zhu T, Xiao Z, Zhang X, Zheng L, Zheng R, Sun Y, Zhou W, Fu Q, Ye X, Chen A, Zheng S, Heidari AA, Wang M, Zhu J, Chen H, Li J. Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:17787-17802. [PMID: 34786302 PMCID: PMC8545238 DOI: 10.1109/access.2021.3052835] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/15/2021] [Indexed: 05/26/2023]
Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
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Affiliation(s)
- Hua Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care MedicineThe 1st Affiliated Hospital, Wenzhou Medical UniversityWenzhou325000China
| | - Tianru Zhu
- The Second Clinical CollegeWenzhou Medical UniversityWenzhou325000China
| | - Zhongxiang Xiao
- Department of PharmacyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xie Zhang
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Long Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Rongwei Zheng
- Department of UrologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Yangjie Sun
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Weilong Zhou
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Qinlei Fu
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xinxin Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Chen
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Shuang Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehran1417466191Iran
- Department of Computer ScienceSchool of ComputingNational University of SingaporeSingapore117417
| | - Mingjing Wang
- Institute of Research and Development, Duy Tan UniversityDa Nang550000Vietnam
| | - Jiandong Zhu
- Department of Surgical OncologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Huiling Chen
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Jifa Li
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
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8
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Duan Z, Wang L, Chen S, Sun M. Instance-based weighting filter for superparent one-dependence estimators. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106085] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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9
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Kumar Shukla P, Kumar Shukla P, Sharma P, Rawat P, Samar J, Moriwal R, Kaur M. Efficient prediction of drug-drug interaction using deep learning models. IET Syst Biol 2020; 14:211-216. [PMID: 32737279 PMCID: PMC8687321 DOI: 10.1049/iet-syb.2019.0116] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 12/23/2022] Open
Abstract
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.
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Affiliation(s)
- Prashant Kumar Shukla
- Department of Computer Science and Engineering, School of Engineering & Technology, Jagran Lake City University (JLU), Bhopal, MP, India
| | - Piyush Kumar Shukla
- Department of Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal, MP, India
| | - Poonam Sharma
- Department of Computer Science & Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Paresh Rawat
- Department of Electronics and Communication Engineering, Sagar Institute of Science & Technology (SISTec), Gandhi Nagar, Bhopal, MP, India
| | - Jashwant Samar
- Department of Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal, MP, India
| | - Rahul Moriwal
- Department of Computer Science and Engineering-AITR Indore, MP, India
| | - Manjit Kaur
- Department of Computer and Communication Engineering, School of Computing and Information Technology, Manipal University Jaipur, Rajasthan, India.
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Liu ZG, Li XY, Qiao LM, Durrani DK. A cross-region transfer learning method for classification of community service cases with small datasets. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105390] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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11
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12
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Chen H, Zhang Q, Luo J, Xu Y, Zhang X. An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105884] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Shi Y, Li X. A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon 2019; 5:e02997. [PMID: 31890956 PMCID: PMC6928309 DOI: 10.1016/j.heliyon.2019.e02997] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/09/2019] [Accepted: 12/04/2019] [Indexed: 11/13/2022] Open
Abstract
Bibliometric analysis is an effective method to carry out quantitative study of academic output to address the research trends on a given area of investigation through analysing existing documents. This paper aims to explore the application of intelligent techniques in bankruptcy predictions so as to assess its progress and describe the research trend through bibliometric analysis over the last five decades. The results indicate that, although there is a significant increase in publication number since the 2008 financial crisis, the collaboration among authors is weak, especially at the international dimension. Also, the findings provide a comprehensive view of interdisciplinary research on bankruptcy modelling in finance, business management and computer science fields. The authors sought to contribute to the theoretical development of bankruptcy prediction modeling by bringing new knowledge and key insights. Artificial intelligent techniques are now serving as important alternatives to statistical methods and demonstrate very promising results. This paper has both theoretical and practical implications. First, it provides insights for scholars into the theoretical evolution and intellectual structure for conducting future research in this field. Second, it sheds light on identifying under-explored machine learning techniques applied in bankruptcy prediction which can be crucial in management and decision-making for corporate firm managers and policy makers.
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Affiliation(s)
- Yin Shi
- Department of Business and Management, Economics and Management Faculty, University Rovira i Virgili, Spain
| | - Xiaoni Li
- Department of Business and Management, Economics and Management Faculty, University Rovira i Virgili, Spain
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14
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Application of Mind Evolutionary Algorithm and Artificial Neural Networks for Prediction of Profile and Flatness in Hot Strip Rolling Process. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-10021-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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Zeng Y, Klabjan D. Online adaptive machine learning based algorithm for implied volatility surface modeling. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2018.08.039] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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16
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Cai Z, Gu J, Wen C, Zhao D, Huang C, Huang H, Tong C, Li J, Chen H. An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:2396952. [PMID: 30034509 PMCID: PMC6032994 DOI: 10.1155/2018/2396952] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 04/02/2018] [Accepted: 05/21/2018] [Indexed: 11/17/2022]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.
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Affiliation(s)
- Zhennao Cai
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jianhua Gu
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
| | - Chunyu Huang
- College of Computer Science and Technology, Changchun University of Science Technology, Changchun 130032, China
| | - Hui Huang
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Changfei Tong
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Jun Li
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Huiling Chen
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
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Liao T, Kuo R. Five discrete symbiotic organisms search algorithms for simultaneous optimization of feature subset and neighborhood size of KNN classification models. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.12.039] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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18
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Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.04.060] [Citation(s) in RCA: 345] [Impact Index Per Article: 43.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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21
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Yang F, Hamit M, Yan CB, Yao J, Kutluk A, Kong XM, Zhang SX. Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:4620732. [PMID: 29065605 PMCID: PMC5394892 DOI: 10.1155/2017/4620732] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 01/09/2017] [Accepted: 02/06/2017] [Indexed: 01/06/2023]
Abstract
Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.
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Affiliation(s)
- Fang Yang
- Department of Medical Engineering, The Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi 830011, China
| | - Murat Hamit
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Chuan B. Yan
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Juan Yao
- Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi 830054, China
| | - Abdugheni Kutluk
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Xi M. Kong
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
| | - Sui X. Zhang
- College of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830011, China
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Yao JT, Onasanya A. Recent Development of Rough Computing: A Scientometrics View. THRIVING ROUGH SETS 2017. [DOI: 10.1007/978-3-319-54966-8_3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Alaminos D, del Castillo A, Fernández MÁ. A Global Model for Bankruptcy Prediction. PLoS One 2016; 11:e0166693. [PMID: 27880810 PMCID: PMC5120822 DOI: 10.1371/journal.pone.0166693] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 11/01/2016] [Indexed: 11/18/2022] Open
Abstract
The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.
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Affiliation(s)
- David Alaminos
- Department of Finance and Accounting, Universidad de Málaga, Málaga, Spain
- * E-mail:
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Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.11.095] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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25
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Du M, Ding S, Jia H. Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.02.001] [Citation(s) in RCA: 246] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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26
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Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D. Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.01.002] [Citation(s) in RCA: 287] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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27
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Derrac J, Chiclana F, García S, Herrera F. Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2015.09.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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Lalbakhsh P, Chen YPP. TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction. ENTERP INF SYST-UK 2015. [DOI: 10.1080/17517575.2015.1090630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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29
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Zhou L, Lu D, Fujita H. The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.04.017] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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A reference model for business intelligence to predict bankruptcy. JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2015. [DOI: 10.1108/jeim-09-2013-0069] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
– Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.
Design/methodology/approach
– To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).
Findings
– This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.
Research limitations/implications
– This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.
Originality/value
– A comprehensive bankruptcy prediction model using BI with customized reporting.
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Cheng MY, Hoang ND. A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2014.12.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Sun J, Li H, Chang PC, He KY. The dynamic financial distress prediction method of EBW-VSTW-SVM. ENTERP INF SYST-UK 2015. [DOI: 10.1080/17517575.2014.986214] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion. ScientificWorldJournal 2014; 2014:157173. [PMID: 24683317 PMCID: PMC3934576 DOI: 10.1155/2014/157173] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 11/10/2013] [Indexed: 11/23/2022] Open
Abstract
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
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Derrac J, García S, Herrera F. Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2013.10.038] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Xu X, Chen HL. Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft comput 2013. [DOI: 10.1007/s00500-013-1089-4] [Citation(s) in RCA: 179] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.02.006] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Pacheco J, Casado S, Angel-Bello F, Álvarez A. Bi-objective feature selection for discriminant analysis in two-class classification. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2013.01.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhou L. Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2012.12.007] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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40
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Differential evolution trained kernel principal component WNN and kernel binary quantile regression: Application to banking. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2012.10.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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41
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Lin SJ, Chang C, Hsu MF. Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2012.11.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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42
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Zhang W, Niu P, Li G, Li P. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2012.10.004] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Shao YH, Deng NY, Yang ZM, Chen WJ, Wang Z. Probabilistic outputs for twin support vector machines. Knowl Based Syst 2012. [DOI: 10.1016/j.knosys.2012.04.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Liu DY, Chen HL, Yang B, Lv XE, Li LN, Liu J. Design of an enhanced fuzzy k-nearest neighbor classifier based computer aided diagnostic system for thyroid disease. J Med Syst 2011; 36:3243-54. [PMID: 22198094 DOI: 10.1007/s10916-011-9815-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Accepted: 11/30/2011] [Indexed: 11/25/2022]
Abstract
In this paper, we present an enhanced fuzzy k-nearest neighbor (FKNN) classifier based computer aided diagnostic (CAD) system for thyroid disease. The neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the particle swarm optimization (PSO) approach. The adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. In addition, we have validated the effectiveness of the principle component analysis (PCA) in constructing a more discriminative subspace for classification. The effectiveness of the resultant CAD system, termed as PCA-PSO-FKNN, has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation (CV) analysis, with the mean accuracy of 98.82% and with the maximum accuracy of 99.09%. Promisingly, the proposed CAD system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.
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Affiliation(s)
- Da-You Liu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
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