## 4 Discussion  
This study established a machine learning model to predict postoperative changes in electrophysiological severity or CTSI scores after surgery for carpal tunnel syndrome. The LightGBM model was effective in predicting electrophysiological severity and the Random Forest model was effective in predicting postoperative changes in CTSI scores. In recent years, there has been an increasing number of reports using AI models for the diagnosis and treatment of CTS [12; 13; 14]. Most of them report on the utility of AI models in the diagnosis of CTS. [PERSON] et al. [12] proposed an AI model to classify the electrophysiological severity of CTS as mild, moderate, and severe based on age, body size, questionnaire responses, and ultrasound findings. Among seven machine learning models, eXtremeGradient Boosting (XGB) was the most accurate [12]. [PERSON] et al. [13] created a machine learning model by generating multiple parameters from DML, motor nerve conduction velocity, SCV, compound motor action potential, and sensory nerve action potential waveforms. They reported that the accuracy of this model in diagnosing CTS was higher than that of conventional NCS. [PERSON] et al. [14] reported that deep learning on the ultrasound image of the median nerve provided a highly accurate diagnosis without nerve conduction studies. On the other hand, there are a few papers regarding the changes in NCS and patient-oriented questionnaires after CTS treatment. [PERSON] et al. [15] used CTSI, NCS, and ultrasound imaging data to predict the cross-sectional area of the median nerve on ultrasound images at one, three, and six months after median nerve hydro-release treatment. The results showed that the multilayer perceptron model performed the best, with R\({}^{2}\) values of 0.906, 0.684, and 0.667 at one-, three- and six months post-treatment, respectively. They also reported that CTSI scores improved after the treatment, but they did not create a machine-learning model to predict the change in post-treatment CTSI. [PERSON] et al. [16] developed a binary classification model to predict whether the rate of Quick DASH improvement after CTR surgery would exceed the minimal clinically important difference (MCID), with predictive accuracies of 0.72 and 0.76 for improvements in functional and symptom scores after CTR surgery, respectively. In the study, they excluded items 7 and 8 among the Quick DASH disability items because those items are related to social activities. [PERSON] et al. [17] compared the responsiveness of patients with the Quick DASH-JSSH and CTSI-JSSH three months after CTR and reported that CTSI-JSSH showed higher responsiveness than DASH-JSSH. Therefore, we selected CTSI-JSSH as a patient-oriented questionnaire. There are several reports on MCIDs for CTSI, with values ranging from 1.04-1.4 for CTSI-SS and 0.4-1.6 for CTSI-FS [18; 19; 20]. [PERSON] et al. [21] reported that MCID cutoff values were not constant and should be  
Figure 5: (**a**) Feature importance; (**b**) SHAP scores of the RF model in predicting the change in CTSI-JSSH-FS scores one year after the surgery. determined based on individual preoperative CTSI scores. The change in CTSI-JSSH score in this study was 1.20 for CTSI-JSSH-SS and 0.99 for CTSI-JSSH-FS, which is comparable to the findings pf previous reports [18, 19, 20, 21]. The RF model showed the highest score in the CTSI-JSSH-SS prediction model. The preoperative CTSI-JSSH score and age were the most important predictors. The RF model also showed the best performance in the prediction of CTSI-JSSH-FS. The important predictors in the RF model were the preoperative CTSI-JSSH score and age. [PERSON] et al. [17] reported that improvements in the CTSI-JSSH score at six months postoperatively correlated with the CTSI-JSSH score itself, rather than with the preoperative electrophysiologic severity. Our results were consistent with their report. In this study, the R\({}^{2}\) values were 0.768 for CTSI-JSSH-SS and 0.846 for CTSI-JSSH-FS.