Machine Learning Techniques for Early Detection of Parkinson's disease

This study explores the application of advanced machine learning techniques for diagnosing Parkinson's Disease (PD) using a comprehensive dataset of 195 records, which includes attributes such name, MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz) , MDVP:Jitter(%), MDVP:Jitter(Abs), MDVP:RAP , MDVP:PPQ, Jitter:DDP, MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5 , MDVP:APQ , Shimmer:DDA, NHR , HNR , status , RPDE , DFA, spread1, spread2 , D2 and PPE. Various algorithms—including Logistic Regression, Support Vector Machine, Random Forest, and XGBoost—were employed to identify the most effective model for accurately predicting calories burned. The results indicate that XGBoost outperforms the other models, achieving the highest accuracy of 97.96% and AUC score, while revealing significant insights into how specific vocal features contribute to the diagnosis of PD. This research underscores the potential of machine learning for improving early detection and management of Parkinson's Disease, offering personalized diagnostic insights that enhance clinical decision-making and patient care.