Abstract: Objective To construct prediction models for early discharge of endometrial cancer patients after enhanced recovery after surgery (ERAS) with random forest and Logistic regression, respectively, and compare the prediction effects of the 2 models. Methods Using convenience sampling, 328 patients with endometrial cancer who underwent ERAS and met the inclusion and exclusion criteria in a tertiary grade-A hospital from January 2019 to December 2021 were randomly assigned to model group and validation group according to the ratio of 7∶3, and the random forest and Logistic regression were used to construct the prediction models of early discharge after ERAS for patients with endometrial cancer. The performance of the 2 models was compared in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, Jorden index, and AUC of ROC. Results In the model group, the accuracy of random forest and Logistic regression was 1.000, and 0.896; sensitivity 1.000, and 0.833; specificity 1.000, and 0.915; positive predictive value 1.000, and 0.750; negative predictive value 1.000, and 0.942; Jorden index 1.000,and 0.729 and AUC 1.000, and 0.950 respectively; in the validation group, the accuracy of random forest and Logistic regression was 0.969, and 0.888; sensitivity 0.960, and 0.750; specificity 0.973, and 0.943; positive predictive value 0.923, and 0.840; negative predictive value 0.986, and 0.904; Jorden’s index 0.933, 0.693, and AUC 0.940, and 0.900, respectively. Conclusion Random forest model outperforms Logistic regression model in predicting early discharge after enhanced recovery after surgery for endometrial cancer patients.

Key words: random forest, Logistic regression, enhanced recovery after surgery, endometrial cancer patients, prediction model, length of stay