## 1 Introduction  
CTS is the most common entrapment neuropathy of the upper extremity and is more common in women, with a peak age range of 40-60 years [1, 2, 3]. Diagnoses are subjectively made based on clinical findings of sensory deficit and/or thenar atrophy [4], while they are made objectively via NCS and ultrasound examination [5]. NCS is useful not only in diagnosing CTS but also in evaluating nerve degeneration. However, NCS is an objective method of assessment and does not always correspond to clinical symptoms [6]. Moreover,electrophysiological recovery does not always support improvements in clinical outcome. Therefore, a patient-orientated questionnaire that assesses patient-perceived symptoms and quality of life (QOL) deterioration is important in determining the best treatment strategy and evaluating the efficacy of the treatment. Performing an NCS and patient-orientated questionnaire pre- and postoperatively is ideal; however, it requires time, comes with high costs, and cooperation from patients; furthermore, in the case of NCSs, patients may experience discomfort. If the postoperative changes determined by the NCS and patient-orientated questionnaires are predicted by preoperative measurements, these problems would be overcome; furthermore, it would also suggest that these approaches are a practical choice for selecting the treatment method.  
The role of nerve conduction studies (NCSs) in carpal tunnel syndrome (CTS) is not only to diagnose CTS but also to objectively evaluate the severity of nerve damage. We have previously reported the usefulness of preoperative evaluation and postoperative assessment using the electrophysiological severity classification (Stages 1-5) based on parameters of distal motor latency (DML) and sensory nerve conduction velocity (SCV) of the median nerve [7]. However, patients' main complaints are pain, numbness, and difficulty with pinching movements, which do not correlate with electrophysiological severity [2]. Therefore, postoperative assessments focusing on patient-oriented outcomes are also important, as postoperative electrophysiologic severity assessment alone is insufficient to determine treatment efficacy. The negative aspects of postoperative NCS are the costs and patient discomfort during the procedure. Also, the use of a patient-oriented questionnaire requires patient cooperation. Therefore, we consider if postoperative changes in NCS and patient-oriented questionnaires could be predicted from preoperative assessments, as this would be ideal in determining the treatment strategy. Currently, machine learning applications in the medical field are expanding [8; 9]. Further improvements in the accuracy of diagnosis and the capability of the prediction of prognoses by machine learning may help us avoid human error in diagnosis and in determining the treatment method. We hypothesized that machine learning, which is a field of artificial intelligence (AI), could predict postoperative electrophysiological severity as well as the scores of patient-oriented questionnaires. In this study, we developed machine learning models to predict postoperative changes in electrophysiological severity, as well as the Carpal Tunnel Syndrome Instrument (CTSI) of the Japanese Society for Surgery of the Hand (JSSH) version; CTSI-JSSH [10].