Stage-Wise Data Balancing Promoting Toe-tapping-based Classification of Parkinson's Disease Progression using Smart Insoles

Abstract

Abstract Given the slow progression nature of Parkinson's Disease (PD), accurate stage classification is predominant for effective disease management. Traditional clinical evaluations, often based on brief physician-patient interactions, can miss nuanced disease progressions. With their continuous monitoring capabilities and bolstered by recent machine learning (ML) advancements, wearable devices such as monitoring insoles (MONI) present a promising solution. However, capturing a comprehensive dataset spanning all PD stages is challenging, leading to data imbalances. These imbalances can cause ML models to favor the majority class, achieving high accuracy but compromising clinical relevance. To address this inherent challenge in PD data, we identified two most effective sampling methods: Synthetic Minority Over-sampling Technique (SMOTE) and Tomek-Links. We compared their data balance performance using toe-tapping datasets collected from PD patients and their age-matched healthy controls (HC) wearing MONI. Additionally, we utilized SHapley Additive exPlanations (SHAP) for ranking to ascertain each technique's efficacy and verify the top factors contributing to PD stage classification, relating these factors to the underlying mechanisms or symptoms of PD. Data balancing enhanced stage classification by 5-7% compared to the original dataset. Our findings show that with SMOTE and Tomek-Links balanced data, we were able not only to improve the accuracy and robustness of stage classification but also observed the model emphasizing features that have a direct relation to each stage rather than favoring the HC majority class, thus ensuring a model is tailored to the PD domain.