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Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women

As osteoporosis is a degenerative disease related to postmenopausal aging, early diagnosis is vital. This study used data from the Korea National Health and Nutrition Examination Surveys to predict a patient’s risk of osteoporosis using machine learning algorithms. Data from 1431 postmenopausal wome...

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Autores principales: Kwon, Youngihn, Lee, Juyeon, Park, Joo Hee, Kim, Yoo Mee, Kim, Se Hwa, Won, Young Jun, Kim, Hyung-Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222287/
https://www.ncbi.nlm.nih.gov/pubmed/35742158
http://dx.doi.org/10.3390/healthcare10061107
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author Kwon, Youngihn
Lee, Juyeon
Park, Joo Hee
Kim, Yoo Mee
Kim, Se Hwa
Won, Young Jun
Kim, Hyung-Yong
author_facet Kwon, Youngihn
Lee, Juyeon
Park, Joo Hee
Kim, Yoo Mee
Kim, Se Hwa
Won, Young Jun
Kim, Hyung-Yong
author_sort Kwon, Youngihn
collection PubMed
description As osteoporosis is a degenerative disease related to postmenopausal aging, early diagnosis is vital. This study used data from the Korea National Health and Nutrition Examination Surveys to predict a patient’s risk of osteoporosis using machine learning algorithms. Data from 1431 postmenopausal women aged 40–69 years were used, including 20 features affecting osteoporosis, chosen by feature importance and recursive feature elimination. Random Forest (RF), AdaBoost, and Gradient Boosting (GBM) machine learning algorithms were each used to train three models: A, checkup features; B, survey features; and C, both checkup and survey features, respectively. Of the three models, Model C generated the best outcomes with an accuracy of 0.832 for RF, 0.849 for AdaBoost, and 0.829 for GBM. Its area under the receiver operating characteristic curve (AUROC) was 0.919 for RF, 0.921 for AdaBoost, and 0.908 for GBM. By utilizing multiple feature selection methods, the ensemble models of this study achieved excellent results with an AUROC score of 0.921 with AdaBoost, which is 0.1–0.2 higher than those of the best performing models from recent studies. Our model can be further improved as a practical medical tool for the early diagnosis of osteoporosis after menopause.
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spelling pubmed-92222872022-06-24 Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women Kwon, Youngihn Lee, Juyeon Park, Joo Hee Kim, Yoo Mee Kim, Se Hwa Won, Young Jun Kim, Hyung-Yong Healthcare (Basel) Article As osteoporosis is a degenerative disease related to postmenopausal aging, early diagnosis is vital. This study used data from the Korea National Health and Nutrition Examination Surveys to predict a patient’s risk of osteoporosis using machine learning algorithms. Data from 1431 postmenopausal women aged 40–69 years were used, including 20 features affecting osteoporosis, chosen by feature importance and recursive feature elimination. Random Forest (RF), AdaBoost, and Gradient Boosting (GBM) machine learning algorithms were each used to train three models: A, checkup features; B, survey features; and C, both checkup and survey features, respectively. Of the three models, Model C generated the best outcomes with an accuracy of 0.832 for RF, 0.849 for AdaBoost, and 0.829 for GBM. Its area under the receiver operating characteristic curve (AUROC) was 0.919 for RF, 0.921 for AdaBoost, and 0.908 for GBM. By utilizing multiple feature selection methods, the ensemble models of this study achieved excellent results with an AUROC score of 0.921 with AdaBoost, which is 0.1–0.2 higher than those of the best performing models from recent studies. Our model can be further improved as a practical medical tool for the early diagnosis of osteoporosis after menopause. MDPI 2022-06-14 /pmc/articles/PMC9222287/ /pubmed/35742158 http://dx.doi.org/10.3390/healthcare10061107 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
Kwon, Youngihn
Lee, Juyeon
Park, Joo Hee
Kim, Yoo Mee
Kim, Se Hwa
Won, Young Jun
Kim, Hyung-Yong
Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title_full Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title_fullStr Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title_full_unstemmed Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title_short Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women
title_sort osteoporosis pre-screening using ensemble machine learning in postmenopausal korean women
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222287/
https://www.ncbi.nlm.nih.gov/pubmed/35742158
http://dx.doi.org/10.3390/healthcare10061107
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