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This project aims to enhance PD detection by integrating feature selection and classification using supervised learning techniques. Two publicly available datasets\u2014the speech and PD classification datasets\u2014are utilized to evaluate model performance across diverse features. The proposed work employs class balancing through the Synthetic Minority Oversampling Technique (SMOTE) to address the issue of class imbalance in this highly unbalanced dataset. Subsequently, the Relief algorithm is used for feature selection to identify the most relevant predictors. An ensemble of models is applied using the RF-XGBoost-KNN classifiers due to their superior accuracy compared to other classifier combinations. The RF-XGBoost-KNN model stack achieved classification accuracies of 94.56% and 93.53% for the PD speech dataset and Parkinson's Disease Classification Dataset, respectively, demonstrating its potential as a robust tool for early and accurate PD diagnosis.<\/jats:p>","DOI":"10.1177\/18724981251353596","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T06:23:42Z","timestamp":1752474222000},"page":"3123-3140","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging relief feature selection and multi-classifier stacking approach for improved Parkinson's disease diagnosis"],"prefix":"10.1177","volume":"19","author":[{"given":"Taezeen","family":"Hamid","sequence":"first","affiliation":[{"name":"Safdarjung enclave, Nauroji Nagar, World Trade Centre, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4843-8883","authenticated-orcid":false,"given":"Megha","family":"Chhabra","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, School of Computing Science &amp; Engineering, Sharda University, Gr. 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