Particle swarm optimization framework for Parkinson's disease prediction

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 achieved 96.7% testing accuracy, an absolute improvement of 2.6% over the best-performing traditional classifier (Bagging classifier at 94.1%), while maintaining exceptional sensitivity (99.0%) and specificity (94.6%). Results were even more significant for Dataset 2, where the PSO model reached 98.9% final accuracy, a 3.9% improvement over the LGBM classifier (95.0%), with near-perfect discriminative capability (AUC = 0.999). These performance gains were achieved with reasonable computational overhead, averaging 250.93 s training time for Dataset 2, suggesting the practical viability of PSO optimization for clinical prediction tasks. Our findings underscore the potential of intelligent optimization techniques in developing practical decision support systems for early neurodegenerative disease detection, with significant implications for clinical practice.