Cargando…
Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach
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 5–10% of all women of reproductive age. The goal of this study was to develop...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317820/ https://www.ncbi.nlm.nih.gov/pubmed/35887611 http://dx.doi.org/10.3390/jpm12071114 |
_version_ | 1784755149885931520 |
---|---|
author | Blass, Ido Sahar, Tali Shraibman, Adi Ofer, Dan Rappoport, Nadav Linial, Michal |
author_facet | Blass, Ido Sahar, Tali Shraibman, Adi Ofer, Dan Rappoport, Nadav Linial, Michal |
author_sort | Blass, Ido |
collection | PubMed |
description | 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 5–10% 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. |
format | Online Article Text |
id | pubmed-9317820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93178202022-07-27 Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach Blass, Ido Sahar, Tali Shraibman, Adi Ofer, Dan Rappoport, Nadav Linial, Michal J Pers Med Article 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 5–10% 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. MDPI 2022-07-07 /pmc/articles/PMC9317820/ /pubmed/35887611 http://dx.doi.org/10.3390/jpm12071114 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Blass, Ido Sahar, Tali Shraibman, Adi Ofer, Dan Rappoport, Nadav Linial, Michal Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title | Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title_full | Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title_fullStr | Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title_full_unstemmed | Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title_short | Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach |
title_sort | revisiting the risk factors for endometriosis: a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317820/ https://www.ncbi.nlm.nih.gov/pubmed/35887611 http://dx.doi.org/10.3390/jpm12071114 |
work_keys_str_mv | AT blassido revisitingtheriskfactorsforendometriosisamachinelearningapproach AT sahartali revisitingtheriskfactorsforendometriosisamachinelearningapproach AT shraibmanadi revisitingtheriskfactorsforendometriosisamachinelearningapproach AT oferdan revisitingtheriskfactorsforendometriosisamachinelearningapproach AT rappoportnadav revisitingtheriskfactorsforendometriosisamachinelearningapproach AT linialmichal revisitingtheriskfactorsforendometriosisamachinelearningapproach |