  Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for
its impact on motor neurons, causing symptoms like tremors, stiffness, and gait
difficulties. This study explores the potential of vocal feature alterations in
PD patients as a means of early disease prediction. This research aims to
predict the onset of Parkinson's disease. Utilizing a variety of advanced
machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost,
and Support Vector Machine, among others, the study evaluates the predictive
performance of these models using metrics such as accuracy, area under the
curve (AUC), sensitivity, and specificity. The findings of this comprehensive
analysis highlight LightGBM as the most effective model, achieving an
impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM
exhibited a remarkable sensitivity of 100% and specificity of 94.43%,
surpassing other machine learning algorithms in accuracy and AUC scores. Given
the complexities of Parkinson's disease and its challenges in early diagnosis,
this study underscores the significance of leveraging vocal biomarkers coupled
with advanced machine-learning techniques for precise and timely PD detection.
