SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making

2026-01-30

AI Summary

This study addresses the limitations of subjective traditional assessments in early Parkinson’s disease diagnosis by proposing a multimodal, interpretable artificial intelligence framework that integrates subjective clinical scales—such as the MDS-UPDRS—with objective motor measurements. Leveraging machine learning models including Random Forest for individualized risk prediction, the approach incorporates SHAP (SHapley Additive exPlanations) to enable feature-level interpretability. Evaluated on the PPMI dataset, the model achieves an accuracy of 98.66% and identifies tremor, bradykinesia, and facial expression as key predictive features. This work establishes a novel paradigm for precise, personalized diagnostic decision-making grounded in both objective and subjective indicators, offering enhanced clinical interpretability and individualized risk assessment.

Technology Category

• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this disease early is challenging because it depends on traditional diagnostic methods that face issues of subjectivity, which commonly delay diagnosis. Several objective analyses are currently in practice to help overcome the challenges of subjectivity; however, a proper explanation of these analyses is still lacking. While machine learning (ML) has demonstrated potential in supporting PD diagnosis, existing approaches often rely on subjective reports only and lack interpretability for individualized risk estimation. This study proposes SCOPE-PD, an explainable AI-based prediction framework, by integrating subjective and objective assessments to provide personalized health decisions. Subjective and objective clinical assessment data are collected from the Parkinson's Progression Markers Initiative (PPMI) study to construct a multimodal prediction framework. Several ML techniques are applied to these data, and the best ML model is selected to interpret the results. Model interpretability is examined using SHAP-based analysis. The Random Forest algorithm achieves the highest accuracy of 98.66 percent using combined features from both subjective and objective test data. Tremor, bradykinesia, and facial expression are identified as the top three contributing features from the MDS-UPDRS test in the prediction of PD.

Problem

• Parkinson's disease
• subjectivity
• objective measurements
• early diagnosis
• interpretability

Innovation

• Explainable AI
• Multimodal integration
• Parkinson's disease prediction
• SHAP interpretability
• Personalized decision-making