2024/08/30 by Elena Marinello, Erica Tavazzi, Marinello, Elena +31
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Air Quality and Health Impacts #Carcinogens and Genotoxicity Assessment #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2408.17376
openalex publication_date 2024/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
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.