ArticleHealthcare (Basel, Switzerland)2022
Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Risk prediction models for postmenopausal osteoporosis: a systematic review and meta-analysis study.BMC musculoskeletal disorders · 2026Pooled it
- Performance of clinical prediction models for identifying postmenopausal osteoporosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- An intelligent auxiliary diagnostic system for early osteoporosis screening using stacking ensemble learning.BMC medical informatics and decision making · 2026Article
- Article
- Article
- Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.Clinical and experimental medicine · 2025Article
- Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.Scientific reports · 2025Article
- Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.BMC women's health · 2025Article
- Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.Scientific reports · 2024Article
- Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.BMC psychology · 2024Article
- Characteristics of the gut microbiota and serum metabolites in postmenopausal women with reduced bone mineral density.Frontiers in cellular and infection microbiology · 2024Article
- Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.Journal of the Chinese Medical Association : JCMA · 2023Article
- Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.Health science reports · 2023Article
- Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database.Journal of bone metabolism · 2023Article
- Gut microbiota signatures and fecal metabolites in postmenopausal women with osteoporosis.Gut pathogens · 2023Article
- Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China.Heliyon · 2023Article
- A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.Healthcare (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.