Machine Learning for Early Detection and Severity Classification in People with Parkinson's Disease

Research overview

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Abstract Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning (ML) models in classifying early and moderate stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-meter walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features—walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of stride length at LWS—achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, stride length at HWS and CV at LWS provided 67.3% accuracy with Naïve Bayes. Walking speed at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve early detection and severity assessment of people of PD.

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