Carpal tunnel syndrome automatic classification: electromyography vs. ultrasound imaging

We study automatic classification for the diagnosis of the Carpal Tunnel Syndrome (CTS), a disease frequently observed in occupational medicine. We apply different classification techniques to two real-life medical data sets related to a group of patients reporting the typical symptoms of this syndrome.We are particularly interested in the performance of “Box-Clustering” (BC), a method that is able to favor readability and interpretation of the results by medical doctors, thanks to its “boxtype” output which naturally configures as a medical report. Preliminary results of a basic implementation of BC applied to different data sets already exist in the literature, and here we add more. In particular, in this paper, we apply a recently developed (and specialized) implementation of BC, and we test it for the first time on real-life medical data related to the CTS. Our purpose is to evaluate the performance of BC for automatic diagnosis, as well as, gain in explanation capability and interpretability. This is, in fact, a crucial aspect in medical applications that generally represents a limit for other well-known and powerful classification techniques.