Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis

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dc.contributor.authorIspizua, Begoña
dc.contributor.authorManjarrés, Diana
dc.contributor.authorNiño-Adan, Iratxe
dc.contributor.editorJiang, Xingpeng
dc.contributor.editorWang, Haiying
dc.contributor.editorAlhajj, Reda
dc.contributor.editorHu, Xiaohua
dc.contributor.editorEngel, Felix
dc.contributor.editorMahmud, Mufti
dc.contributor.editorPisanti, Nadia
dc.contributor.editorCui, Xuefeng
dc.contributor.editorSong, Hong
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T11:00:04Z
dc.date.available2024-09-10T11:00:04Z
dc.date.issued2023
dc.descriptionPublisher Copyright: © 2023 IEEE.
dc.description.abstractMetabolic syndrome (MetS) is considered to be a major public health problem worldwide leading to a high risk of diabetes and cardiovascular diseases. In this paper, data collected by the Precision Medicine Initiative of the Basque Country, named the AKRIBEA project, is employed to infer via Machine Learning (ML) techniques the features that have the most influence on predicting MetS in the general case and also separately by gender. Different Feature Normalization (FN) and Feature Weighting (FW) methods are applied and an exhaustive analysis of explainability by means of Shapley Additive Explanations (SHAP) and feature relevance methods is performed. Validation results show that the Extreme Gradient Boosting (XGB) with Min-Max FN and Mutual Information FW achieves the best trade-off between precision and recall performance metrics.en
dc.description.statusPeer reviewed
dc.format.extent4
dc.identifier.citationIspizua , B , Manjarrés , D & Niño-Adan , I 2023 , Prediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysis . in X Jiang , H Wang , R Alhajj , X Hu , F Engel , M Mahmud , N Pisanti , X Cui & H Song (eds) , Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 . Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 , Institute of Electrical and Electronics Engineers Inc. , pp. 1989-1992 , 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 , Istanbul , Turkey , 5/12/23 . https://doi.org/10.1109/BIBM58861.2023.10385780
dc.identifier.citationconference
dc.identifier.doi10.1109/BIBM58861.2023.10385780
dc.identifier.isbn9798350337488
dc.identifier.urihttps://hdl.handle.net/11556/5008
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85184906046&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
dc.relation.ispartofseriesProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsclassification
dc.subject.keywordsexplainability
dc.subject.keywordsfeature relevances
dc.subject.keywordsmachine learning
dc.subject.keywordsmetabolic syndrome
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsComputer Vision and Pattern Recognition
dc.subject.keywordsAutomotive Engineering
dc.subject.keywordsModeling and Simulation
dc.subject.keywordsHealth Informatics
dc.titlePrediction of Metabolic Syndrome Based on Machine Learning Techniques with Emphasis on Feature Relevances and Explainability Analysisen
dc.typeconference output
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