Machine Learning Models Highlight the Impact of Pollution and Weather Patterns on Relapse Occurrence in Multiple Sclerosis Patients

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. Relapses have been linked to environmental factors such as the weather and pollutant concentrations in the air, but the exact relationship between these phenomena is still unclear. In this study, we investigated the role of environmental factors in predicting imminent relapse occurrence in MS patients, leveraging clinical and environmental data collected over a period of one week preceeding the possible event, using data collected in the context of the H2020 BRAINTEASER project. To do this, we developed and tested a range of combinations of predictive models (logistic regression, LR; and random forest, RF) and feature selection schemes, both manual and data-driven. The RF model trained after a data-driven feature selection process based on the Variable Importance in Projection (VIP) metric yielded the best results, i.e., an AUC-ROC of 0.713 and an AUC-PR of 0.639. We identified several key predictors, including clinical variables such as time since MS onset, age at onset, diagnostic delay, and the Expanded Disability Status Scale (EDSS) score, and environmental variables such as wind speed, precipitation, NO2, PM10, average and maximum temperatures, and humidity. These findings suggest that environmental factors may be viable predictors of imminent relapse occurrence in MS.