A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers.

ABSTRACT:

Introduction

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.

Methods

The study included a total of 558 patients (329 with EMs and 229 with AD), in whom key hematological and coagulation markers were analy

SUBMITTER: Wang W 

PROVIDER: S-EPMC11428011 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature