Towards Multiple Sclerosis Personalised Interventions Based on Real-World Predictive Analytics
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.
Authors
- Nikolaos Grigoriadis (ORCID: https://orcid.org/0000-0002-4278-3301)
- Alexandra Anagnostopoulou (ORCID: https://orcid.org/0000-0001-9578-7552)
- Alexandros Moraitopoulos (ORCID: https://orcid.org/0000-0002-4527-3833)
- Georgios Petridis
- Panagiotis D. Bamidis (ORCID: https://orcid.org/0000-0002-9936-5805)
- Antonis Billis (ORCID: https://orcid.org/0000-0002-1854-7560)
- Ioannis Nikolaidis (ORCID: https://orcid.org/0000-0002-4054-7559)
- Charis Styliadis (ORCID: https://orcid.org/0000-0001-7383-2439)
- Konstantinos Aggelopoulos (ORCID: https://orcid.org/0009-0000-5295-0946)
Institutions
- Aristotle University of Thessaloniki (GR)
- Ippokrateio General Hospital of Thessaloniki (GR)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250603
- Primary Topic
- Multiple Sclerosis Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hellenic Foundation for Research and Innovation