Machine Learning Distinguishes Gait Patterns in MS

The gait patterns of patients with multiple sclerosis (MS) were found to be discriminable from those of healthy controls using machine learning methods, according to an article published in BioMedical Engineering OnLine. The study utilized a standard set of gait features with a support vector machine learning model to differentiate the gaits of patients with MS from those of healthy controls with an accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%. When an additional set of novel gait features (toe direction, hull area, base of support area, foot length, foot width, and foot area) were added to the support vector machine, the accuracy increased to 88%, recall to 90%, and F1-score to 93%. The precision level was unchanged. "These results demonstrate that machine learning models trained on new features from raw walkway data can more effectively separate patient and control targets and could potentially be served as an alternative method for identifying gait abnormalities in MS," the authors said.