Evaluation of Cognitive Impairment in Pediatric Multiple Sclerosis with Machine Learning: An Exploratory Study of miRNA Expressions

Multiple Sclerosis (MS) is a demyelinating autoimmune disease that usually affects young adults; however, recently some symptoms of cognitive impairment have been recognized as early signs of MS onset in pediatric patients (PedMS). The underlying relationships between these two conditions, as well as their molecular markers, have not been fully understood yet. In this work, we analyze microRNAs (miRNAs) expression profiles of PedMS patients with machine learning algorithms in order to create effective models able to detect the presence of cognitive impairment. In particular, we compare three different classification algorithms, fed with features automatically selected by a feature selection strategy. Experimental results show that linear support vector machines achieved the best performance. Moreover, we discuss the importance of ten of the most discriminant automatically selected miRNAs. A graphical analysis of these features highlights the relationships among miRNAs and the two classes the patients belongs to.