A Hybrid Approach based on Machine Learning Classifiers and Harris Hawk Optimization for Parkinson Disease Classification

The rapid advancements in artificial intelligence (AI) and data analytics have created significant opportunities in fields such as healthcare and intelligent transportation. As the volume of complex data continues to grow, there is an increasing demand for analytical models capable of extracting meaningful patterns and generating accurate predictions. This study focuses on enhancing Parkinson’s disease (PD) detection by using the Harris Hawk Optimization (HHO) for feature selection to improve classifier performance on the UCI Parkinson's disease dataset. We evaluated four classifiers: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), under two scenarios: without feature selection and with HHO-based feature selection. The results reveal substantial performance improvements with HHO, with RF achieving the highest accuracy of 98.33%. Comparisons with recent studies highlight the effectiveness of our approach, establishing it as a new benchmark in PD detection accuracy. This research underscores the essential role of optimized feature selection in enhancing classifier accuracy and reliability, especially for early diagnosis through voice-based data.