{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T13:48:22Z","timestamp":1769176102584,"version":"3.49.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Endometriosis (EMs) and adenomyosis (AD) are common gynecological diseases that impact women's health, and they share symptoms such as dysmenorrhea, chronic pain, and infertility, which adversely affect women's quality of life. Current diagnostic approaches for EMs and AD involve invasive surgical procedures, and thus, methods of noninvasive differentiation between EMs and AD are needed. This retrospective cohort study introduces a novel, noninvasive classification methodology employing a stacked ensemble machine learning (ML) model that utilizes peripheral blood and coagulation markers to distinguish between EMs and AD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study included a total of 558 patients (329 with EMs and 229 with AD), in whom key hematological and coagulation markers were analyzed to identify distinctive profiles. Feature selection was conducted through ML (logistic regression, support vector machine, and K-nearest neighbors) to determine significant hematological markers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Red cell distribution width, mean corpuscular hemoglobin concentration, activated partial thromboplastin time, international normalized ratio, and antithrombin III were proved to be the key distinguishing indexes for disease differentiation. Among all the ML classification models developed, the stacked ensemble model demonstrated superior performance (area under the curve = 0.803, 95% credibility interval = 0.701\u20130.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Integrating biomarkers into this multi-algorithm framework offers a novel approach to noninvasive diagnosis. These results advocate for the application of stacked ensemble ML utilizing cost-effective and readily available peripheral blood and coagulation indicators for the early, rapid, and noninvasive differential diagnosis of EMs and AD, offering a potentially transformative approach for clinical decision-making and personalized treatment strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2024.1463419","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:15:32Z","timestamp":1725945332000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers"],"prefix":"10.3389","volume":"6","author":[{"given":"Weiying","family":"Wang","sequence":"first","affiliation":[]},{"given":"Weiwei","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Sen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1007\/s43032-021-00527-0","article-title":"A detailed study in adenomyosis and endometriosis: evaluation of the rate of coexistence between uterine adenomyosis and die according to imaging and histopathology findings","volume":"28","author":"Alborzi","year":"2021","journal-title":"Reprod Sci"},{"key":"B2","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/S0140-6736(21)00389-5","article-title":"Endometriosis is a chronic systemic disease: clinical challenges and novel innovations","volume":"397","author":"Taylor","year":"2021","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1056\/NEJMra1810764","article-title":"Endometriosis","volume":"382","author":"Zondervan","year":"2020","journal-title":"N Engl J Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.fertnstert.2008.04.056","article-title":"High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners","volume":"92","author":"Meuleman","year":"2009","journal-title":"Fertil Steril"},{"key":"B5","doi-asserted-by":"publisher","first-page":"hoac009","DOI":"10.1093\/hropen\/hoac009","article-title":"Eshre guideline: endometriosis","volume":"2022","author":"Becker","year":"2022","journal-title":"Hum Reprod Open"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1016\/j.cell.2021.04.041","article-title":"Endometriosis: etiology, pathobiology, and therapeutic prospects","volume":"184","author":"Saunders","year":"2021","journal-title":"Cell"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1093\/humupd\/dmab017","article-title":"Adenomyosis pathogenesis: insights from next-generation sequencing","volume":"27","author":"Bulun","year":"2021","journal-title":"Hum Reprod Update"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/humrep\/dead229","article-title":"Proposal for targeted, neo-evolutionary-oriented, secondary prevention of early-onset endometriosis and adenomyosis. 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