Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression
This work addresses relapse prediction for MS patients, but it is incremental as it applies standard machine learning methods to a new dataset.
The study tackled predicting relapse occurrence in Multiple Sclerosis patients by analyzing environmental pollutants and clinical data, achieving a best AUC-ROC score of 0.713 with Random Forest.
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.