Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression

TL;DR

Predictive models, including Random Forest and Logistic Regression, were employed to predict the occurrence of relapses based on clinical and pollutant data collected over a week, and the RF yielded the best result, with an AUC-ROC score of 0.713.

Abstract

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.

Figures (1)

• Figure 1: AUC-ROC as a function of the number of features in Backward Feature Selection, presented as mean and standard deviation.