Interpretable Machine Learning for Predicting Multiple Sclerosis Conversion from Clinically Isolated Syndrome

Abstract

Background: Machine learning (ML) prediction of clinically isolated syndrome (CIS) conversion to multiple sclerosis (MS) could be used as a remote, preliminary tool by clinicians to identify high-risk patients that would benefit from early treatment. Objective: This study evaluates ML models to predict CIS to MS conversion and identifies key predictors. Methods: Five supervised learning techniques (Naive Bayes, Logistic Regression, Decision Trees, Random Forests and Support Vector Machines) were applied to clinical data from 138 Lithuanian and 273 Mexican CIS patients. Seven different feature combinations were evaluated to determine the most effective models and predictors. Results: Key predictors common to both datasets included sex, presence of oligoclonal bands in CSF, MRI spinal lesions, abnormal visual evoked potentials and brainstem auditory evoked potentials. The Lithuanian dataset confirmed predictors identified by previous clinical research, while the Mexican dataset partially validated them. The highest F1 score of 1.0 was achieved using Random Forests on all features for the Mexican dataset and Logistic Regression with SMOTE Upsampling on all features for the Lithuanian dataset. Conclusion: Applying the identified high-performing ML models to the CIS patient datasets shows potential in assisting clinicians to identify high-risk patients.