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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1463419

A Stacked Machine Learning-Based Classification Model for Endometriosis and Adenomyosis: A Retrospective Cohort Study Utilizing Peripheral Blood and Coagulation Markers

Provisionally accepted
Weiying Wang Weiying Wang 1Weiwei Zeng Weiwei Zeng 2*Sen Yang Sen Yang 2
  • 1 Shanghai Jiao Tong University, Shanghai, China
  • 2 Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

The final, formatted version of the article will be published soon.

    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. 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 machine learning (logistic regression, support vector machine, and K-nearest neighbors) to determine significant hematological markers (red cell distribution width, mean corpuscular hemoglobin concentration, activated partial thromboplastin time, international normalized ratio, and antithrombin III) critical for disease differentiation. Among all the machine learning classification models developed, the stacked ensemble model demonstrated superior performance (area under the curve=0.803, 95% credibility interval=0.701-0.904). Our findings demonstrate the effectiveness of the stacked ensemble ML model for classifying EMs and AD. Integrating biomarkers into this multialgorithm 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.

    Keywords: Endometriosis, Adenomyosis, Peripheral Blood, coagulation markers, machine learning

    Received: 11 Jul 2024; Accepted: 29 Aug 2024.

    Copyright: © 2024 Wang, Zeng and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Weiwei Zeng, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.