Background Endometriosis is a common progressive female health disorder in which tissues similar to the lining of the uterus grow on other parts of the body like ovaries, fallopian tubes, bowel, and other parts of reproductive organs. In women, it is one of the most common causes of pelvic pain and infertility. In the US, one in every ten women of reproductive age group has endometriosis. The actual cause of endometriosis is still unknown, and it is quite difficult to diagnose. There are several theories regarding the cause; however, not a single theory has been scientifically proven. Methods In this paper, we try to identify the drivers of endometriosis’ diagnoses via leveraging advanced Machine Learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a great extent, if likelihood of endometriosis can be predicted well in advance. As a result, the proper medical care and treatment can be given to the impacted patients. To demonstrate the feasibility, Logistic Regression (LR) and eXtreme Gradient Boosting (XGB) algorithms were trained on 36 months of medical history data. Results The machine learning models were used to predict the likelihood of disease on qualified patients from the healthcare claims patient level database. Several directly and indirectly features were identified as important in accurate prediction of the condition onset, including selected diagnosis and procedure codes. Conclusions Leveraging the machine learning approaches can aid early prediction of the disease and offer an opportunity for patients to receive the needed medical treatment earlier in the patient journey. Creating a typing tool that can be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers could further aid the objective of improving the diagnosis activities and inform the diagnostic processes that would result in timely and precise diagnosis, ultimately increasing patient care and quality of life.

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.