Bridging Predictive Modeling and Clinical Interpretability: An Explainable AI Approach to Parkinson’s Disease Detection

Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, predominantly affecting older adults. Early detection is crucial, as subtle motor and non-motor symptoms frequently overlap with other conditions, often resulting in delayed diagnosis. Many existing models rely on costly and less accessible imaging modalities such as MRI or PET scans, limiting their applicability in resource-constrained settings where only routine clinical data are available. This study develops interpretable AI models for early PD detection using structured clinical variables, incorporating feature selection techniques. Feature selection was conducted via Random Forest (RF) importance ranking combined with SelectKBest statistical scoring, retaining the most informative predictors for modeling. Five classifiers were implemented in parallel: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), RF, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The RF model achieved the highest accuracy of 92.72% (ROC-AUC: 0.968), with CNN and SVM showing competitive performance. LSTM exhibited balanced sensitivity and specificity, while KNN demonstrated relatively lower recall. To improve clinical interpretability, LIME was applied to each model to produce instance-level explanations, consistently highlighting tremor severity, motor impairment, cognitive scores, and age as key influential features. These results demonstrate that structured clinical variables alone can enable reliable PD detection without dependence on imaging. Integrating explainable artificial intelligence enhances transparency and supports responsible clinical adoption.