  Background and Objective: Parkinson's disease (PD) is the second most common
progressive neurological condition after Alzheimer's, characterized by motor
and non-motor symptoms. Developing a method to diagnose the condition in its
beginning phases is essential because of the significant number of individuals
afflicting with this illness. PD is typically identified using motor symptoms
or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are
expensive, time-consuming, and unavailable to the general public; furthermore,
they are not very accurate. These constraints encouraged us to develop a novel
technique using SHAP and Hard Voting Ensemble Method based on voice signals.
Methods: In this article, we used Pearson Correlation Coefficients to
understand the relationship between input features and the output, and finally,
input features with high correlation were selected. These selected features
were classified by the Extreme Gradient Boosting (XGBoost), Light Gradient
Boosting Machine (LightGBM), Gradient Boosting, and Bagging. Moreover, the Hard
Voting Ensemble Method was determined based on the performance of the four
classifiers. At the final stage, we proposed Shapley Additive exPlanations
(SHAP) to rank the features according to their significance in diagnosing
Parkinson's disease. Results and Conclusion: The proposed method achieved
85.42% accuracy, 84.94% F1-score, 86.77% precision, 87.62% specificity, and
83.20% sensitivity. The study's findings demonstrated that the proposed method
outperformed state-of-the-art approaches and can assist physicians in
diagnosing Parkinson's cases.
