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Disentangling Blood-Based Markers of Multiple Sclerosis Through Machine Learning: An Evaluation Study

Robin Vlieger*, Mst Mousumi Rizia, Abolfazl Amjadipour, Nicolas Cherbuin, Anne Brüstle, Hanna Suominen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Paperpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMEDINFO 2025 - Healthcare Smart x Medicine Deep
Subtitle of host publicationProceedings of the 20th World Congress on Medical and Health Informatics
EditorsMowafa S. Househ, Mowafa S. Househ, Zain Ul Abideen Tariq, Mahmood Al-Zubaidi, Uzair Shah, Elaine Huesing
PublisherIOS Press BV
Pages1766-1767
Number of pages2
ISBN (Electronic)9781643686080
DOIs
Publication statusPublished - 7 Aug 2025
Event20th World Congress on Medical and Health Informatics, MEDINFO 2025 - Taipei, Taiwan
Duration: 9 Aug 202513 Aug 2025

Publication series

NameStudies in Health Technology and Informatics
Volume329
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference20th World Congress on Medical and Health Informatics, MEDINFO 2025
Country/TerritoryTaiwan
CityTaipei
Period9/08/2513/08/25

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