Male and Female Hormone Reading to Predict Pregnancy Percentage Using a Deep Learning Technique: A Real Case Study

Abstract

Diagnosing gynecological diseases is a significant difficulty for the medical sector. Numerous patients visit gynecological clinics for pregnancies as well as for other illnesses, such as polycystic ovarian syndrome, ovarian cysts, endometritis, menopause, and others. In relation to pregnancy, patients, whether they are men, women, or both, may experience a variety of issues. As a result, in this research, we developed a proposed method that makes use of artificial neural networks (ANN) to help gynecologists predict the success rate of a pregnancy based on the reading of the pregnancy hormone ratio in the blood. The ANN was used in this test in the lab as a group of multiple perceptrons or neurons at each layer; however, in the final hidden layer, the genetic algorithm (GA) and Bat algorithm were used instead. These two algorithms are fit and appropriate for optimizing the models that are aimed to estimate or predict a value. As a result, the GA attempts to determine the testing cost using equations and the Bat algorithm attempts to determine the training cost. To improve the performance of the ANN, the GA algorithm collaborates with the Bat algorithm in a hybrid approach in the hidden layer of ANN; therefore, the pregnancy prediction result of using this method can be improved, optimized, and more accurate. Based on the flexibility of each algorithm, gynecologists can predict the success rate of a pregnancy. With the help of our methods, we were able to run experiments using data collected from 35,207 patients and reach a classification accuracy of 96.5%. These data were gathered from the Department of Obstetrics and Gynecology at the Hospital University of Jordan (HUJ). The proposed method aimed to predict the pregnancy rate of success regardless of whether the data are comprised of patients whose pregnancy hormones are in the normal range or of patients that suffer from factors favoring sterility, such as infections, malformations, and associated diseases (e.g., diabetes).