{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T07:56:40Z","timestamp":1790409400346,"version":"4.1.0"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ISF","award":["Grant 2753\/20"],"award-info":[{"award-number":["Grant 2753\/20"]}]},{"name":"ISF","award":["Grant 2021"],"award-info":[{"award-number":["Grant 2021"]}]},{"name":"The Louise and Alan Edwards Foundation, Clinical Research Fellowship","award":["Grant 2753\/20"],"award-info":[{"award-number":["Grant 2753\/20"]}]},{"name":"The Louise and Alan Edwards Foundation, Clinical Research Fellowship","award":["Grant 2021"],"award-info":[{"award-number":["Grant 2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Endometriosis is a condition characterized by implants of endometrial tissues into extrauterine sites, mostly within the pelvic peritoneum. The prevalence of endometriosis is under-diagnosed and is estimated to account for 5\u201310% of all women of reproductive age. The goal of this study was to develop a model for endometriosis based on the UK-biobank (UKB) and re-assess the contribution of known risk factors to endometriosis. We partitioned the data into those diagnosed with endometriosis (5924; ICD-10: N80) and a control group (142,723). We included over 1000 variables from the UKB covering personal information about female health, lifestyle, self-reported data, genetic variants, and medical history prior to endometriosis diagnosis. We applied machine learning algorithms to train an endometriosis prediction model. The optimal prediction was achieved with the gradient boosting algorithms of CatBoost for the data-combined model with an area under the ROC curve (ROC-AUC) of 0.81. The same results were obtained for women from a mixed ethnicity population of the UKB (7112; ICD-10: N80). We discovered that, prior to being diagnosed with endometriosis, affected women had significantly more ICD-10 diagnoses than the average unaffected woman. We used SHAP, an explainable AI tool, to estimate the marginal impact of a feature, given all other features. The informative features ranked by SHAP values included irritable bowel syndrome (IBS) and the length of the menstrual cycle. We conclude that the rich population-based retrospective data from the UKB are valuable for developing unified machine learning endometriosis models despite the limitations of missing data, noisy medical input, and participant age. The informative features of the model may improve clinical utility for endometriosis diagnosis.<\/jats:p>","DOI":"10.3390\/jpm12071114","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T22:11:47Z","timestamp":1657231907000},"page":"1114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach"],"prefix":"10.3390","volume":"12","author":[{"given":"Ido","family":"Blass","sequence":"first","affiliation":[{"name":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tali","family":"Sahar","sequence":"additional","affiliation":[{"name":"Alan Edwards Pain Management Unit, McGill University Health Centre, Montreal, QC H3G 1A4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adi","family":"Shraibman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Academic College of Tel Aviv-Yaffo, Tel Aviv 69978, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5136-8014","authenticated-orcid":false,"given":"Dan","family":"Ofer","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be\u2019er Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7218-2558","authenticated-orcid":false,"given":"Nadav","family":"Rappoport","sequence":"additional","affiliation":[{"name":"Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9357-4526","authenticated-orcid":false,"given":"Michal","family":"Linial","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Be\u2019er Sheva 84105, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1056\/NEJMcp1000274","article-title":"Clinical practice. 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