A novel machine learning-based proposal for early prediction of endometriosis disease

作     者：Enamorado-Diaz, Elena Morales-Trujillo, Leticia Garcia-Garcia, Julian-Alberto Marcos, Ana T. Navarro-Pando, Jose Escalona-Cuaresma, Maria-Jose 

作者机构：Univ Seville Grp ES3 Engn & Sci Software Syst Grp Ave Reina Mercedes Seville 41012 Spain G7innovat Co Genet Unit Calle Radio Sevilla Seville 41001 Spain Inst Estudio Biol Reprod Humana INEBIR Catedra Reprod & Genet Humana Seville Spain Univ Europea Atlantico UNEATLANTICO Santander Spain Fdn Univ Iberoamericana FUNIBER Seville Spain San Juan de Dios Hosp Seville Spain 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (Expert Sys Appl)

年 卷 期：2025年第271卷

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：ERDF, EU. [PID2022-137646OB-C31, MICIU/AEI/10.13039/501100011033] ESF+ [MICIU/AEI/10.13039/501100011033, PREP2022-000332] 021/C005/00151010 

主　　题：Endometriosis Predictive model Machine learning algorithm Clinical Decision Support System 

摘      要：Pyrethroids, which are widely utilized in agriculture, household products, and public health for their potent insecticidal properties, elicit significant concerns regarding their potential endocrine-disrupting effects. However, previous studies have yielded inconsistent data, largely due to the absence of a standardized screening system. To address this limitation, the present study introduces an Integrated Approach to Testing and Assessment (IATA) to evaluate the endocrine-disrupting potential of pyrethroids, aligned with the Adverse Outcome Pathway (AOP) framework. Employing this IATA-based methodology, the endocrine-disrupting effects of five pyrethroids, allethrin, phenothrin, deltamethrin, cypermethrin, and lambda-cyhalothrin-were investigated, with a focus on hormone levels of 17(3-estradiol (E2) and testosterone (T). Enzyme-linked immunosorbent assays (ELISA) and receptor transactivation assays were utilized to assess the direct receptor interactions and alternative disruption mechanisms. The results demonstrated that lambda-cyhalothrin and phenothrin significantly elevated E2 levels, while all tested compounds substantially reduced T levels. Notably, transactivation assays indicatedBackground: Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods: This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model in