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

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.