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Parkinson's disease (PD) is a prevalent neurodegenerative disorder known forits impact on motor neurons, causing symptoms like tremors, stiffness, and gaitdifficulties. This study explores the potential of vocal feature alterations inPD patients as a means of early disease prediction. This research aims topredict the onset of Parkinson's disease. Utilizing a variety of advancedmachine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost,and Support Vector Machine, among others, the study evaluates the predictiveperformance of these models using metrics such as accuracy, area under thecurve (AUC), sensitivity, and specificity. The findings of this comprehensiveanalysis highlight LightGBM as the most effective model, achieving animpressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBMexhibited a remarkable sensitivity of 100% and specificity of 94.43%,surpassing other machine learning algorithms in accuracy and AUC scores. Giventhe complexities of Parkinson's disease and its challenges in early diagnosis,this study underscores the significance of leveraging vocal biomarkers coupledwith advanced machine-learning techniques for precise and timely PD detection.

Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms