[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118479-en":3,"doc-seo-118479-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118479,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features","Parkinson’s disease (PD) is a progressive neurodegenerative condition that affects motor and non-motor functions, making early, reliable diagnosis essential. This study develops a stacked ensemble learning prediction system for PD from telemonitoring vocal attributes, addressing imbalanced data and feature optimization. An open-access dataset (22 vocal attributes, 195 instances, 31 subjects) is split by subjects to avoid leakage, preprocessed with cleaning and min–max scaling, and balanced via SMOTE applied only to training. Feature selection uses forward search, gain ratio, and Kruskal–Wallis with subject-wise cross-validation. Base models (SVM, RF, KNN, DT) are combined using logistic regression as the meta-classifier, evaluated with recording-wise and subject-wise metrics.","Omodunbi, Bolaji A. , Olawade, David, Awe, Omosigho F. , Soladoye, Afeez A. , Aderinto, Nicholas, Ovsepian, Saak V. and Boussios, Stergios (2025) Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features.  \nDiagnostics, 15 (12) . p. 1467.  \nDownloaded from: [https://ray.yorksj.ac.uk/id/eprint/12165/](https://ray.yorksj.ac.uk/id/eprint/12165/)  \nThe version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [https://doi.org/10.3390/diagnostics15121467](https://doi.org/10.3390/diagnostics15121467)  \nResearch at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repositories Policy Statement  \nRaY  \nResearch at the University of York St John For more information please contact RaY at  \n[ray@yorksj.ac. uk](ray@yorksj.ac. uk)  \nArticle  \nStacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features  \nBolaji A. Omodunbi 1, David B. Olawade 2,3,4,5, *, Omosigho F. Awe 6, Afeez A. Soladoye 1, Nicholas Aderinto 7, Saak V. Ovsepian 8,9 and Stergios Boussios 3,10,11,12,13,14,15,16  \nAcademic Editors: Dechang Chen and Fakhar Abbas  \nReceived: 8 March 2025  \nRevised: 28 May 2025  \nAccepted: 5 June 2025  \nPublished: 9 June 2025  \nCitation: Omodunbi, B.A.; Olawade, D.B.; Awe, O.F.; Soladoye, A.A.; Aderinto, N.; Ovsepian, S.V.; Boussios, S. Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features. Diagnostics 2025, 15, 1467. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics15121467](doi.org/10.3390/diagnostics15121467)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Computer Engineering, Federal University Oye-Ekiti, Oye-Ekiti 371104, Nigeria; [bolaji.omodunbi@fuoye.edu.ng](bolaji.omodunbi@fuoye.edu.ng) (B.A.O.); [afeez.soladoye@fuoye.edu.ng](afeez.soladoye@fuoye.edu.ng) (A.A.S.)  \n2 Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK  \n3 Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; [stergiosboussios@gmail.com](stergiosboussios@gmail.com)  \n4 Department of Public Health, York St John University, York YO31 7EX, UK  \n5 School of Health and Care Management, Arden University, Arden House, Middlemarch Park, Coventry CV3 4FJ, UK  \n6 Department of Computer Engineering, Federal University of Technology Akure, Gaga 340110, Nigeria; [ofawe@futa.edu.ng](ofawe@futa.edu.ng)  \n7 Department of Medicine and Surgery, Ladoke Akintola University of Technology, Ogbomoso 210214, Nigeria; [nicholasoluwaseyi6@gmail.com](nicholasoluwaseyi6@gmail.com)  \n8 Faculty of Engineering and Science, University of Greenwich London, Chatham ME4 4TB, UK; [s.v.ovsepian@greenwich.ac.uk](s.v.ovsepian@greenwich.ac.uk)  \n9 Faculty of Medicine, Tbilisi State University, Tbilisi 0177, Georgia  \n10 Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT2 7PB, UK  \n11 Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK  \n12 Kent Medway Medical School, University of Kent, ","cbCaisyPdaWqviK2","https://ap.wps.com/l/cbCaisyPdaWqviK2","pdf",375679,1,19,"English","en",105,"# Abstract\n## Background and objective\n## Methods and data processing\n## Feature selection and modeling\n## Evaluation and results","[{\"question\":\"What problem does the study address for Parkinson’s disease diagnosis?\",\"answer\":\"The study targets the need for early and accurate PD diagnosis by building a machine-learning prediction system based on telemonitoring vocal features.\"},{\"question\":\"How does the study prevent data leakage during model evaluation?\",\"answer\":\"Subjects are separated into distinct training (22 subjects) and testing (9 subjects) groups, ensuring no subject appears in both sets.\"},{\"question\":\"What models are used in the stacked ensemble framework?\",\"answer\":\"The framework uses SVM, random forest, K-nearest neighbor, and decision tree as base classifiers, with logistic regression as the meta-classifier.\"}]","Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features | 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