Factors affecting endometriosis in women of reproductive age: The differences between the results of neural network and logistic regression

Abstract

Background: Endometriosis is a common gynecologic problem in women of reproductive age around the globe. The aim of this study was to specify the factors influencing endometriosis in women of reproductive age using logistic regression and artificial neural network (ANN). Methods: The data of this case-control study was obtained from the medical records in Rasoul-e-Akram hospital, Tehran. Patients, who underwent laparoscopy from 2007 to 2015 and were diagnosed with endometriosis, were selected as the case group (n = 250), and patients diagnosed without endometriosis served as controls (n = 250). To investigate the factors affecting the occurrence of endometriosis, ANN and logistic regression were used and for evaluating the efficiency of the two methods, the area under the ROC curve (AUC) was used. To analyze the data, SPSS (version 22) and R (version 3.2.1) software were used. Results: The means of age in the cases (34.84 ± 0.62) and controls (33.75 ± 0.55) were significantly different (P value = 0.02). With multiple logistic regression, the number of live births and premenstrual spotting were found to be the factors associated with the occurrence of endometriosis. The most important variables entering ANN included BMI, menstrual duration, age, and premenstrual spotting. Conclusions: The results showed that the fitted ANN with AUC of 0.94 could predict the likelihood of endometriosis better than logistic regression with AUC of 0.72. This suggests the superiority of ANN to the logistic regression and proposes ANN be used in further research on predicting the risk of endometriosis, instead of logistic regression. The most important factors affecting endometriosis in this model were BMI, menstrual duration, age, and premenstrual spotting that have to be considered in the clinical settings.