Informatics in Medicine Unlocked · Published 2026-07-29 · DOI 10.1016/j.imu.2026.101795
Wijdan Rashid Abdulhussien, Noor F. Abbas, Zahraa G. Mustafa, Mustafa Noaman Kadhim, Aqeel H. Al-Fatlawi, Hayder Najm, Dhulfiqar Zoltán Alwahab
Parkinson's disease (PD) is a progressive neurological disorder that affects speech production, making speech-based analysis an effective tool for early diagnosis. Although machine learning (ML) techniques have shown promising performance in PD detection, their performance is often affected by high-dimensional feature spaces and the presence of redundant or irrelevant voice features. However, conventional feature selection methods often produce unstable feature subsets and may not consistently identify the most informative voice features. To address these challenges, this study proposes a Consensus-Based Multi-Metaheuristic Feature Selection framework integrated with lightweight supervised machine learning, termed CMFS-LSML. The proposed framework combines Particle Swarm Optimization (PSO), the Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) within a consensus-driven voting mechanism, where features selected by at least two of the three optimization algorithms are retained and evaluated using the combined classification performance of Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Logistic Regression (LR) classifiers as the optimization objective. Experimental results demonstrate effective feature reduction while maintaining high classification performance. It achieves 98.31% accuracy using 9 features (60.87% reduction) on the Parkinson Disease Classification Dataset and 95.83% accuracy using 10 features (77.27% reduction) on the Parkinson Dataset with Replicated Acoustic Features. Compared with recent studies, the proposed method improves the reduction ratio by approximately 13.05% and 18.18%, respectively, while maintaining higher or comparable accuracy. Furthermore, compared with using the full feature set, the proposed approach reduces the average execution time by 77.25%, resulting in lightweight ML models suitable for real-time PD diagnosis and resource-constrained healthcare environments.
Abstract from DOAJ. Public domain (CC0 1.0).
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Abdulhussien, W., Abbas, N., Mustafa, Z., et al. (2026). CMFS-LSML: Consensus-Based multi-metaheuristic feature selection with lightweight supervised machine learning for enhanced Parkinson disease classification from voice features. Informatics in Medicine Unlocked. https://doi.org/10.1016/j.imu.2026.101795