Machine Learning Methods for the Classification of Endometriosis

Abstract

Endometriosis is a benign disorder estimated to affect 10% of women during their reproductive years. The main method for the diagnosis of endometriosis currently used by the clinicians is laparoscopic inspection followed by histological confirmation. Non-invasive methods such as ultrasound or magnetic resonance imaging do not have enough diagnostic power. The aim of this study was to find a potential panel of serum biomarkers for the diagnostic classification of endometriosis. A total of 35 serum biomarkers were measured in cases and controls. The controls in the study were relatively older women who come to the clinic for sterilization, which resulted in left- skewed patient population for age. In order to evaluate sensitivity in respect to this confounder, a matched and non-matched subset of subjects were compared in the analysis. The data was further stratified randomly into training and test data sets. Machine learning analysis was performed using multivariate approaches (Naive Bayes, Support Vector Machine using linear or polynomial kernel, Random Forest, Elastic net, Artificial Neural Network) in training and test data sets separately to train and validate the findings using different cross validation techniques. The comparison of the machine learning approaches suggested that Random Forest and Elastic Net perform particularly well in comparison to the other methods. The predictive biomarkers identified in the study included not only the conventional endometriosis marker CA125, but also novel potential biomarkers which can refine currently utilized practices in the diagnosis and classification of endometriosis