AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.4255
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ORIGINAL RESEARCH ARTICLE

Discovering predictive features of multiple sclerosis from clinically isolated syndrome with machine learning

Minh Sao Khue Luu1†* ,  Bair N. Tuchinov1,2† ,  Anna I. Prokaeva2,3† ,  Denis S. Korobko2,3 ,  Nadezhda A. Malkova2,3 ,  Andrey A. Tulupov1,2
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1 Stream Data Analytics and Machine Learning Laboratory, Novosibirsk State University, Novosibirsk, Russia
2 The Institute International Tomography Center of the Russian Academy of Sciences, Novosibirsk, Russia
3 State Novosibirsk Regional Clinical Hospital, Novosibirsk, Russia
AIH, 4255 https://doi.org/10.36922/aih.4255
Submitted: 16 July 2024 | Accepted: 26 August 2024 | Published: 24 September 2024
© 2024 by the Author (s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Accurately predicting the progression of clinically isolated syndrome (CIS) to multiple sclerosis (MS) is crucial for early intervention and management. This study employs a range of machine learning models, including categorical boosting, extreme gradient boosting, light gradient boosting machine, random forest, support vector machine, and logistic regression, to classify CIS patients based on their likelihood of developing MS. Our best model achieves and demonstrates superior predictive accuracy of 0.9312, measured using the area under the curve metric. In addition, we apply explainability techniques to determine the most influential features driving the predictions, identifying which CISs are most indicative of MS progression. Furthermore, we explore feature interactions to detect relationships between features, providing a deeper understanding of the underlying mechanisms. The study utilizes public data from 273 CISs patients, offering significant contributions to the clinical management and early diagnosis of MS.

Keywords
Clinically isolated syndromes
Multiple sclerosis
Machine learning
Binary classification
Predictive features
Model explainability
Funding
This work was supported by a grant from the Russian Science Foundation (RSF 23-15-00377).
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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