Classification of patients with early-stage multiple sclerosis and healthy controls using kinematic analysis during a dual-task

Abstract

Multiple sclerosis (MS) is the disabling neurological disease that currently most affects young people. Changes in gait significantly impact the functionality and independence of these individuals. This study aimed to differentiate between patients in the early stages of MS and healthy controls using machine learning in angular gait variables. This cross-sectional observational study included 38 participants, 19 with MS and 19 in the healthy control group (without neurological or orthopedic diseases). For movement analysis, a three-dimensional gait examination was conducted on patients with EDSS (Expanded Disability Status Scale) scores below 3.5 and healthy volunteers during normal gait and while performing a dual task (walking and performing a working memory task). An elastic net regression model was utilized to classify patients and healthy controls based on the kinematic variables. Our model achieved an AUC (area under the curve) of the ROC plot = 0.77 ± 0.21 using the average, an AUC of 0.94 ± 0.09 using the average and standard deviation, and AUC = 0.95 ± 0.09 when incorporating only the standard deviation of kinematic variables. The study suggests that utilizing angular gait analysis with machine learning methods is an effective approach to categorizing individuals with early-stage multiple sclerosis and healthy controls.