Towards Multiple Sclerosis Personalised Interventions Based on Real-World Predictive Analytics.
Journal:
Studies in health technology and informatics
Published Date:
May 15, 2025
Abstract
This study investigates the use of machine learning (ML) techniques to predict intervention response in patients with Multiple Sclerosis (PwMS) using real-world data from wearable devices. Data from 27 PwMS, monitored over two months were analyzed with several state-of-the-art ML models to predict the efficacy of a computerized cognitive intervention targeting cognitive decline. A model based on Support Vector Machines achieved high accuracy in identifying patient response within the first 2-3 weeks of intervention, aided by feature selection methods like Mutual Information and Recursive Feature Elimination. Early prediction capability enables timely therapeutic adjustments, enhancing personalization of treatment and improving patient quality of life.