In the context of the H2020 BRAINTEASER project, in collaboration with the University of Pavia and the other consortium partners, we are investigating the possible impact of air pollution on the progression of Multiple Sclerosis (MS).
Starting from the literature, where some potential correlations between pollutant agents and MS emerged, we are analysing the retrospective data provided within the project and made available to the whole research community here.
We explored the use of different machine learning techniques, including both linear and non-linear approaches, combined with the use of manual or automatic techniques for identifying the most robust features for prediction.
From our preliminary analyses on MS, the combination of dynamic environmental features with essential clinical variables appeared to be effective for enhancing the accuracy of predictive models when forecasting the occurrence of a relapse, i.e., an exacerbation of the symptoms. Noticeably, the role of environmental features was confirmed via all used feature selection approaches.
