2813 Clinical Prediction of Unsuccessful Endometrial Ablation: Random Forest vs Logistic Regression

Abstract

Study Objective To compare the performance of the prediction models of surgical re-intervention within 2 years after endometrial ablation (EA) by a multivariate random forest model vs the previously presented multivariate logistic regression model. Design Retrospective cohort study, minimal follow-up time of 2 years. Setting Data from Catharina Hospital, Eindhoven and Elkerliek Hospital, Helmond, both non-university teaching hospitals in the Netherlands, were used. Patients or Participants Pre-menopausal women (18+) who have had an EA for heavy menstrual bleeding between January 2004 & April 2013. A total number of 446 patients were eligible for analysis. Interventions Used ablation methods were Cavatherm (Veldana Medical SA, Morges, Switzerland), Gynecare Thermachoice (Ethicon, Sommerville, US.) and Thermablate EAS (Idoman, Ireland). Used interventions and other ablation techniques had the same outcomes according to previously published literature. Measurements and Main Results Data-analysis was done by using IBM SPSS statistics software version 21.0 (IBM Corp., Armonk, NY, USA). The random forest model was trained in MATLAB (2018b) using the TreeBagger function in the Statistics and Machine Learning Toolbox. The prediction model based on a multivariate logistic regression analysis had an AUC of 0.71. The machine learning model had an AUC of 0.63 and an AUC of 0.65 after hyperparameter optimization. Conclusion Based on the preliminary results, we can conclude that the random forest model in this case is not better than the logistic regression model to predict the outcome of surgical re-intervention within two years after EA. In summary, the performance of a random forest clinical prediction model is not necessarily superior to a logistic regression model. The performance of each model is influenced by the sample size, the number of predictors, hyperparameter tuning and the linearity of associations.