Voice analisis as a digital biomarker: a machine learning approach for automated multiple sclerosis classification

Abstract

Voice analysis is a non-invasive tool that can capture subtle motor impairments in Multiple Sclerosis (MS). The objective of this study is to develop and validate a machine learning (ML) framework for the automated classification of MS through acoustic voice analysis. A cohort of 300 gender-balanced participants (200 with MS and 100 healthy controls) provided sustained vocal recordings. Fifteen acoustic features were extracted. An elastic network model first identified the most relevant parameters from the development cohort, which were then used to train five supervised ML classifiers.