Abstract

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 non-CDMS 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, Infratentorial MRI, and Oligoclonal Bands. The study's clinical implications highlight ML's potential in enhancing early MS prognosis, aiding timely interventions. The Random Forest model, with its robust performance, emerges as a valuable tool for identifying patients at risk.

While the study contributes to predictive analytics in neurological disorders, limitations include cohort specificity and retrospective data use. Prospective studies and further exploration of data sources are recommended for broader applicability.

This research demonstrates the efficacy of ML in early MS progression prediction, providing clinicians with a promising tool for personalized patient care. The findings contribute to advancing predictive healthcare analytics and emphasize the significance of tailored interventions in neurological disorders.

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