Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning

Carpal Tunnel Syndrome (CTS) is the most common peripheral neuropathy affecting the hand function. Although the most complains from patients with CTS are fine motor control failures in daily manual activities, parameters of hand functional control have not been considered neither in current diagnostic nor evaluation process. In addition, CTS has been identified as an occupational disease. Over 50% of reported CTS cases are work related. However, early screening protocols of CTS at a preliminary stage are absent, and thus unable to prevent further complications, especially for high-risk populations who can advance their CTS stage on daily work basis. In the current protocol, we aim to identify important parameters of hand functional control that are indicative of CTS clinical occurrence and severity stages. Based on designed experiments during hand grasping, we performed machine learning classifiers to detect, filter and subtract important biomarkers or groups of biomarkers that dominantly classify the CTS hand and its severity. The identified biomarkers not only provide a high potential of a paradigm shift in CTS management, but also are able to shed light on hand functional evaluations associated with this neuropathy. In this paper, we adopt one of machine learning approaches, random forests, to the raw experimental hand function gripping data to identify the most important biomarkers for CTS. The experimental results show the effectiveness of the proposed work.