A disease diagnosis system for smart healthcare based on fuzzy clustering and battle royale optimization

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dc.authoridHirota, Kaoru/0000-0001-5347-6182
dc.contributor.authorYan, Fei
dc.contributor.authorHuang, Hesheng
dc.contributor.authorPedrycz, Witold
dc.contributor.authorHirota, Kaoru
dc.date.accessioned2024-05-19T14:46:40Z
dc.date.available2024-05-19T14:46:40Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe ongoing growth of the Internet of Things and machine learning technology have provided increased motivation for the development of smart healthcare. In this study, a disease diagnosis system is proposed for remote identification and early prediction in smart healthcare environments. The originality of this study resides in the innovative implementation of ensuing modules to improve diagnostic accuracy of the system. First, fuzzy clustering based on the forest optimization algorithm is employed to detect outliers and a self-organizing fuzzy logic classifier is applied to supplement missing data in electronic medical records (EMRs). A feature selection technique using the battle royale optimization algorithm is then developed to remove redundant information and identify optimal EMR features. The refined and fused data are further classified using an eigenvalue-based machine learning algorithm to determine whether a patient exhibits a certain disease. Simulation experiments are conducted with widely used heart disease and diabetes datasets to evaluate the performance of the proposed system, using accuracy, precision, recall, and F-measure as evaluation metrics.en_US
dc.description.sponsorshipJilin Provincial Department of Science and Technology, China [20210201075GX]en_US
dc.description.sponsorshipThis work was supported by the Jilin Provincial Department of Science and Technology, China under Grant 20210201075GX.en_US
dc.identifier.doi10.1016/j.asoc.2023.111123
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85179122336en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.asoc.2023.111123
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5572
dc.identifier.volume151en_US
dc.identifier.wosWOS:001135542900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSmart Healthcareen_US
dc.subjectDisease Diagnosisen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectFuzzy Logic Classifieren_US
dc.subjectBattle Royale Optimizationen_US
dc.titleA disease diagnosis system for smart healthcare based on fuzzy clustering and battle royale optimizationen_US
dc.typeArticleen_US

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