Prediction of Disease Progression in Multiple Sclerosis

This study demonstrates the potential of machine learning to predict multiple sclerosis (MS) progression, using clinical data to simulate real-world hospital conditions with missing values. The model highlights key predictors such as EDSS scores, functional systems, and CNS functions during relapses, with predictive accuracy evolving over time. Despite lower precision in predicting secondary progressive MS, the approach shows promise as a second opinion tool for physicians, although the results need validation in larger datasets with fewer missing values and additional data, like MRI results.