Feature extraction on patient satisfaction prediction using TKA intraoperative data

<p>Total knee arthroplasty (TKA) is a surgery to decrease knee pain and to improve walking ability by replacing a knee deformed by osteoarthritis or rheumatoid arthritis with an artificial joint. The patient satisfaction of TKA reported to be 75% to 89% and was lower than total hip arthroplasty (THA). The patient satisfaction can be quantified by using postoperative patient satisfaction called KSS2011. Conventional studies have investigated patient satisfaction prediction using preoperative patient information. In addition to the preoperative data, this study employs two kinds of intraoperative data after removing anterior crucial ligament (ACL), and after implanting prothesis according to the progress of TKA surgery. It enables us to optimize intraoperative conditions to maximize the patient satisfaction during TKA operation. We introduce two kinds of feature extraction methods, and three kinds of models to predict the patient satisfaction after TKA. The experimental results on 62 patients (male:17, female:45) showed that KSS2011 prediction with the proposed method achieved the minimum root-mean-squared-error (RMSE) of 6.15, 6.05. 5.75, on preoperative, after ACL removal, and after implanting, respectively. Furthermore, we investigated the importance of features with SHAP. It indicated that implant GAP was the most important feature to predict patient satisfaction.</p>