Enhancing Parkinson's Disease Diagnosis: A Stacking Ensemble Approach Leveraging Machine Learning Techniques

Accurate diagnosis of Parkinson's Disease (PD) is crucial for effective management and timely intervention. However, traditional diagnostic methods often struggle to differentiate among Parkinsonian variants, leading to diagnostic errors. To address this challenge, we propose an Enhanced Stacking Ensemble (ESE) model that leverages machine learning techniques to enhance PD diagnosis. The ESE model integrates multiple algorithms through a stacking ensemble approach, with five base learners (XGB, SGD, ET, GB, and KNN) optimized using Bayesian hyperparameters. By combining outputs from the base learners, the stacking ensemble model improves predictive accuracy. Simulation results demonstrate superior performance compared to traditional ensemble methodologies and single classifiers, highlighting the effectiveness of our approach in enhancing PD diagnosis. This advancement shows promise in enhancing the clinical reliability of PD diagnosis, ultimately leading to improved patient outcomes and more informed medical decision-making.