Feature Optimization and Stacked Ensemble Learning for Parkinson's Disease Classification Using Speech Analysis - Agrawal, Sneha; Sahu, Satya Prakash (Technical review - IETE, 2025)
Parkinson's Disease (PD) is the second most common neurodegenerative disorder, whose symptoms worsen over time, making early diagnosis a challenging task. Changes in speech have been identified as an early symptom of PD identification. However, medical datasets often have a small sample size, while speech signal analysis generates high-dimensional data. Therefore, rigorous feature selection is essential for obtaining the best set of PD characteristics. This paper proposes a hybrid filter-wrapper feature selection approach for PD classification using a publicly available speech dataset (188 PD, 64 healthy subjects). Maximum Relevancy Minimum Redundancy (mRMR) and Relief algorithms are used to select top-ranked features, followed by the Modified Whale Optimization Algorithm (mWOA) to refine the selection for obtaining an optimized feature subset. The class imbalance issue is addressed using SMOTE. A stacked ensemble model is developed, integrating base learners, Decision Tree, Support Vector Machine, Naïve Bayes, k-Nearest Neighbour, and deep networks like shallow and deep with hyperparameters tuned via a grid search mechanism. The proposed approach is evaluated against state-of-the-art methods based on accuracy, precision, recall, and F1-score. Results demonstrate that hybrid feature selection and hyperparameter tuning reduce computational burden while improving classification accuracy, making it a promising framework for PD detection from speech data.