Machine-learning-derived prediction models of recurrence of ovarian endometriosis after laparoscopic surgery

Endometriosis is a long-term health problem that affects a significant number of women globally. Among the various forms of endometriosis, ovarian endometriosis (OEM) is the most prevalent. This research aimed to investigate the factors contributing to the recurrence of OEM after laparoscopic conservative surgery and develop a predictive model utilizing machine learning techniques. The clinical data of 338 patients diagnosed with OEM who underwent laparoscopic conservative surgery at Wuhan University Renmin Hospital between January 2020 and January 2023 were retrospectively analyzed. During a 2-year follow-up period, patients were categorized into either the recurrence group or the non-recurrence group based on the incidence of disease recurrence. Chi-square and Spearman analysis were implemented to identify the factors related to postoperative recurrence in patients with OEM. Statistically significant factors were selected to construct the correlation models. Four algorithms were used in model construction: Random Forest, Gaussian Process, Extreme Gradient Boosting, and Multilayer Perceptron. The primary metric for evaluating model performance was the area under the receiver operating characteristic curve. Sixteen variables were associated with postoperative recurrences. The Gaussian Process had the best predictive power and the area under the receiver operating characteristic curve of the test set was 0.90. The test dataset for the Gaussian Process revealed a sensitivity of 0.75, specificity of 0.90, positive predictive value of 0.46, negative predictive value of 0.97, and accuracy rate of 0.88. The predictive model for the Gaussian Process developed in this study effectively assessed the risk of postoperative recurrence in patients with OEM.