Vocal Biomarkers for Parkinson’s Disease Classification through Hybrid Feature Selection with Beluga Whale Optimization

ntroduction
Parkinson's disease (PD) is a progressive neurodegenerative disorder that often causes vocal impairments. As a result, voice analysis is a promising avenue for early and objective diagnosis. While voice is a well-established biomarker, the high dimensionality of acoustic feature sets can lead to model overfitting. This necessitates effective feature selection strategies.
Objective
The objective is to validate a hybrid feature selection pipeline that combines statistical filters with a metaheuristic wrapper algorithm. The aim is to identify a minimal, yet highly discriminative, set of acoustic features for PD classification. This approach aims to strike a balance between dimensionality reduction and high diagnostic accuracy.
Methods
This study validates a hybrid feature selection pipeline. It combines statistical filters such as Spearman correlation (Corr) and mutual information (MI) with a metaheuristic beluga whale optimization (BWO) wrapper. The framework's efficacy was benchmarked across a diverse suite of machine learning classifiers. Evaluation was conducted using 10-fold stratified group cross-validation on a dataset of 40 PD patients and 40 healthy controls. The process dynamically identifies parsimonious feature subsets within each validation fold.
Results
When paired with a random forest classifier, the Corr+MI+BWO pipeline achieved a peak F1-score of 0.966 using an average of only 21.2 features. This represents a 73% reduction in dimensionality with a minimal 2.4% performance trade-off compared to a standard filter-based approach. The most consistently selected features proved to be both statistically significant (P < 0.05) and part of a clinically coherent signature of Parkinsonian dysarthria.
Conclusion
The proposed hybrid framework is an effective methodology that successfully balances model simplicity with high predictive accuracy. This research provides a strong foundation for the development of objective, noninvasive tools for the early detection and monitoring of PD.