EXPLAINABLE TEMPORAL TRANSFORMER FOR DISEASE PROGRESSION PREDICTION USING ATTENTION AND SHAP ANALYSIS

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the gradual deterioration of motor functions, significantly affecting the quality of life of patients. Accurate prediction of disease progression is essential for timely clinical inter vention, treatment planning, and personalized patient management. Traditional machine learning approaches often treat clinical observations as independent samples, limiting their ability to capture the temporal dynamics inherent in longitudinal patient data. Al though deep learning models such as Long Short-Term Memory (LSTM) networks have demonstrated improved temporal modeling capabilities, they may struggle to effectively capture long-range dependencies present in disease progression trajectories. This thesis presents an Explainable Feature-Aware Enhanced Temporal Transformer (FAETT) framework for predicting Parkinson’s disease progression using longitudinal voice-based biomarkers from the Parkinson Telemonitoring dataset obtained from the UCI Machine Learning Repository. The proposed framework integrates temporal sequence modeling with self-attention mechanisms to learn complex relationships across historical patient ob servations. A comprehensive preprocessing pipeline involving data cleaning, feature scal ing, temporal sequence generation, and patient-wise train-test partitioning is employed to ensure robust model development and unbiased evaluation. To assess the effectiveness of the proposed approach, FAETT is compared against three baseline models: Random Forest (RF), Long Short-Term Memory (LSTM), and a stan dard Transformer architecture. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Experimental results demonstrate that the proposed FAETT model achieves supe rior predictive performance, attaining an MAE of 1.2688, RMSE of 1.6457, and R² score of 0.9754, outperforming the baseline approaches. The findings indicate that the incor poration of feature-aware temporal attention significantly enhances the model’s ability to capture disease progression patterns. iii To address the interpretability requirements of clinical decision-support systems, SHAP (SHapley Additive exPlanations) analysis is integrated into the framework. The explain ability analysis identifies the most influential vocal biomarkers contributing to disease progression prediction and provides transparent insights into model behavior. Correlation analysis, residual diagnostics, and prediction-performance visualizations further validate the robustness and reliability of the proposed framework. The results demonstrate that the proposed FAETT architecture effectively combines pre dictive accuracy with model interpretability, making it a promising tool for explainable disease progression forecasting in Parkinson’s disease. The study highlights the potential of attention-based temporal learning and explainable artificial intelligence techniques for advancing data-driven healthcare applications.