Particle swarm optimization framework for Parkinson's disease prediction.

ABSTRACT: Early diagnosis of Parkinson's disease (PD) is challenging due to subtle initial symptoms. This study introduces an advanced machine learning framework that leverages particle swarm optimization (PSO) to improve PD detection through vocal biomarker analysis. Our novel approach unifies the optimization of both acoustic feature selection and classifier hyperparameter tuning within a single computational architecture. We systematically evaluated PSO-enhanced predictive models for PD detection using two comprehensive clinical datasets. Dataset 1 includes 1,195 patient records with 24 clinical features, and Dataset 2 comprises 2,105 patient records with 33 multidimensional features spanning demographic, lifestyle, medical history, and clinical assessment variables. For Dataset 1, the PSO model

SUBMITTER: Eliwa EHI 

PROVIDER: S-EPMC12453757 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature