Factors Affecting Endometriosis in Women of Reproductive Age: The Differences Between the Results of Neural Network and Logistic Regression

Shahla Chaichian, Jamileh Abolghasemi, Fatemeh Omidi, Shahnaz Rimaz, Zahra Najmi, Abolfazl Mehdizadehkashi, Bahram Moazzami
In: Shiraz E-Medical Journal · 2018 · vol. In Press(In Press) · doi:10.5812/semj.62560 · W2885995317
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study used logistic regression and neural networks to identify factors influencing endometriosis in reproductive-aged women, comparing their predictive performance.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study compared neural network and logistic regression models to identify factors associated with endometriosis in women of reproductive age, using a case–control design with variables including age, BMI, menstrual history, dysmenorrhea, dyspareunia, reproductive history, infertility, and spotting-related symptoms. Both modeling approaches highlighted age, BMI, reproductive factors, and premenstrual spotting as influential, with logistic regression reporting an AUC of 0.940 and showing age (OR ~1.043), BMI (OR ~1.11), premenstrual spotting, dyspareunia, dysmenorrhea, infertility history, and aspects of live birth history as statistically associated with the response in the reported tables. The authors note that some variables were only selected if significant at an “80% level,” indicating potential thresholding for inclusion and a limited discussion of uncertainty across all predictors. This paper is centrally about endometriosis — it models and compares risk-factor associations for endometriosis using neural networks and logistic regression in reproductive-age women.

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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.

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endometriosis

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