Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach

Abstract

9 Background: Balance impairment affects over 75% of individuals with multiple sclerosis (MS), 1 0 and leads to an increased risk of falling. Numerous postural sway metrics have been shown to be 1 1 sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines 1 2 concerning the most appropriate postural sway metrics to monitor impairment. This investigation 1 3 implemented a machine learning approach to assess the accuracy and feature importance of 1 4 various postural sway metrics to differentiate individuals with MS from healthy controls as a 1 5 function of physiological fall risk.

Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach

“…The use of machine learning techniques has profoundly influenced healthcare research (Sumeet Dua, 2014). Many detection algorithms have been used with various types of features for the diagnosis of different disorders or physical conditions including Parkinson's Disease (Abdulhay et al, 2018;Sankar, 2016), multiple sclerosis (Sun, Hsieh and Sosnoff, 2018;McGinnis et al, 2017) or Alzheimer's Disease (Jin and Deng, 2018).…”