A Foundational Framework for Voice-based Parkinsonian Biomarkers: Integrating Hybrid Deep Learning with Dual-Layer Explainability for Future Clinical Reference
Parkinson's Disease (PD) is a progressive neurodegenerative disorder, which also impacts speech and vocal characteristics at the initial stages of the disorder. This study proposes an explainable hybrid Bi-LSTM and XG-Boost for detection Parkinsons Disease using non-invasive voice recording, and is designed to be interpretable. The Bidirectional Long Short-Term Memory (Bi-LSTM) network is employed to learn deep latent representations from vocal biomarkers and the final classification is done by the XG-Boost network using both the deep latent representations and the handcrafted voice features. It includes Explainable Artificial Intelligence (XAI) methods like SHAP and LIME to improve interpretability and transparency. SHAP is to identify the globally important vocal features that affect the model prediction, while LIME gives the explanations of individual predictions by instances. The purpose of this work is not to develop a complete clinical implementation tool, as deployment in healthcare and real world will require a detailed clinical validation and regulatory clearance. Rather, in this research we will develop an explainable and transparent AI framework that can be the basis for future researchers and developers to design robust clinical support systems and healthcare applications. The proposed approach has the potential to incorporate the deep learning, ensemble learning and explainable AI concepts in an interpretable Parkinson's disease prediction.