Predictive modeling of vocal biomarkers for the diagnosis of Parkinson's disease.

Parkinson's disease (PD) is among the two most prevalent neurodegenerative disorders (NDDs), affecting about 2-3% of individuals aged 65 and older. This NDD exhibits characteristic motor symptoms and several other non-motor features. Vocal deficits have been identified as one of the earliest quantifiable indicators of PD, which makes speech evaluation a viable, painless diagnostic instrument. We aim to apply machine learning (ML) models to vocal biomarkers for the early detection of PD, and use explainable artificial intelligence (XAI) techniques to interpret the predictions. The dataset is from Kaggle, a publicly reputable database, containing 1000 Parkinson's samples and 24 acoustic variables. We performed feature selection to identify the crucial vocal biological markers. Multiple machine learning (ML) models: Adaptive Boosting (AdaBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), LightGBM (LGBM), CatBoost, Gradient Boosting (GB), Histogram-Based Gradient Boosting (HGB), and K-Nearest Neighbors (KNN) were employed. We also used SHAP (Shapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plot (PDP) to explain the model performances. The HGB model ranked highest (1.00) based on accuracy, precision, recall, and F1-score, respectively. Also, the Confidence intervals (CI) (1.00,1.00) and p-value of < 0.001 of HGB were computed. XAI showed that jitter and shimmer-based biomarkers were the strongest contributors to the prediction of PD. In this study, the results showed that vocal base biomarker screening is not only economical but also an accessible diagnostic tool. In subsequent studies, we hope to include more varied datasets to improve both model and therapeutic relevance.