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

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

Journal

Artificial intelligence in health
ISSN: 3029-2387

Output data

Year: 2024, Volume: 1, Number: 4, Pages: 107-122 Pages count : 16 DOI: 10.36922/aih.4255

Authors

Luu Minh Sao Khue ¹ , Tuchinov Bair N. ^1,2 , Prokaeva Anna I. ^2,3 , Korobko Denis S. ^2,3 , Malkova Nadezhda A. ^2,3 , Tulupov Andrey A. ^1,2

Affiliations

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

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.

Luu M.S.K. , Tuchinov B.N. , Prokaeva A.I. , Korobko D.S. , Malkova N.A. , Tulupov A.A.
Discovering predictive features of multiple sclerosis from clinically isolated syndrome with machine learning
Artificial intelligence in health. 2024. V.1. N4. P.107-122. DOI: 10.36922/aih.4255 OpenAlex

Submitted:	Jul 16, 2024
Accepted:	Aug 26, 2024
Published online:	Sep 24, 2024

≡ OpenAlex:

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