Machine Learning Algorithms for Early Prediction of Multiple Sclerosis Progression: A Comparative Study

Multiple Sclerosis (MS) is a chronic autoimmune disease characterized by central nervous system (CNS) degeneration, leading to diverse neurological symptoms. Managing MS poses a challenge due to its unpredictable progression. This study focuses on early prediction of MS progression using machine learning (ML) algorithms, comparing the effectiveness of Random Forest, XGBoost, Decision Tree, and Logistic Regression. Clinical, genetic, and environmental factors were analyzed in a cohort of Mexican mestizo patients recently diagnosed with Clinically Isolated Syndrome (CIS). Data preprocessing addressed missing values, and feature selection tailored to the population’s characteristics was applied. The dataset was split into training and testing sets, maintaining stratification for CDMS and nonCDMS cases. Machine learning models were trained with optimized hyperparameters. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were employed. The Random Forest model exhibited superior performance (AUC: 0.93, accuracy: 87%), outperforming other models. Variable importance analysis identified top predictors, including Periventricular MRI, Age, Infraten