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

• URL: http://arxiv.org/abs/2601.22516v1
• Date: Fri, 30 Jan 2026 03:49:31 GMT
• Title: SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making
• Authors: Md Mezbahul Islam, John Michael Templeton, Masrur Sobhan, Christian Poellabauer, Ananda Mohan Mondal,
• Abstract summary: Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors.<n>Machine learning (ML) has demonstrated potential in supporting PD diagnosis, but existing approaches often rely on subjective reports only.<n>This study proposes SCOPE-PD, an explainable AI-based prediction framework, by integrating subjective and objective assessments.
• Score: 1.4572472675272603
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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