Poster
Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study
Authors: Vlieger, Robin; Rizia, Mst Mousumi; Amjadipour, Abolfazl; Cherbuin, Nicolas; Brüstle, Anne; Suominen, Hanna
Editors: Househ, Mowafa S.; Tariq, Zain Ul Abideen; Al-Zubaidi, Mahmood; Shah, Uzair; Huesing, Elaine
Conference name: World Congress on Medical and Health Informatics
Publisher: IOS Press
Publication year: 2025
Journal: Studies in Health Technology and Informatics
Book title : MEDINFO 2025 — Healthcare Smart × Medicine Deep: Proceedings of the 20th World Congress on Medical and Health Informatics
Journal name in source: Studies in health technology and informatics
Series title: Studies in Health Technology and Informatics
Number in series: 329
Volume: 329
First page : 1766
Last page: 1767
eISBN: 978-1-64368-608-0
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI251204
Web address : https://doi.org/10.3233/shti251204
Studies of blood-based markers in multiple sclerosis using machine learning for classification use widely varying methods. Here different configurations of machine learning algorithms, feature selection methods, and evaluation approaches were compared. Logistic Regression with Random Forests for feature selection and 10-fold cross-validation classified best, features depended on selection methods, and cross-validation data splits were heterogeneous. This suggests experimental setups influence classification and selected markers.
Funding information in the publication:
This research was funded by the ANU OHIOH initiative, which aims to transform healthcare by developing new personalised health technologies and solutions in collaboration with patients, clinicians, and healthcare providers.
