Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases with a severe impact on patients' lives. Both diseases create significant psychological and economic burdens due to alternating acute phases requiring hospital and home care. One possible solution could be the employment of sensor data to develop predictive models that can assist clinicians in making treatment and therapeutic decisions. In the context of the iDPP@CLEF 2024 challenge, this work aims to develop and compare different machine-learning approaches for predicting the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) scores in ALS patients, and relapses in MS patients, using wearable and environmental data, respectively. Specifically, the analysis focuses on the impact of these data and seeks to determine whether their incorporation enhances predictive performance. The results showed that there is indeed an improvement in the models' performance when sensor data are considered, in both the disease. In particular, in the case of ALS the Root Mean Square Error (RMSE) range, over the predicted twelve ALSFRS-R score, improved from [0.463-0.733] to [0.286-0.582] when incorporating the wearable data, as well as in the case of MS, where the inclusion of environmental data has improved the prediction of relapse, with the RMSE decreasing from 72.992 to 69.564.