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Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Authors
 Tae Keun Yoo  ;  Sung Kean Kim  ;  Deok Won Kim  ;  Joon Yul Choi  ;  Wan Hyung Lee  ;  Ein Oh  ;  Eun-Cheol Park 
Citation
 YONSEI MEDICAL JOURNAL, Vol.54(6) : 1321-1330, 2013 
Journal Title
 YONSEI MEDICAL JOURNAL 
ISSN
 0513-5796 
Issue Date
2013
MeSH
Aged ; Artificial Intelligence* ; Bone Density/physiology* ; Female ; Humans ; Middle Aged ; Osteoporosis, Postmenopausal
Keywords
Screening ; clinical decision tools ; machine learning ; risk assessment ; support vector machines
Abstract
PURPOSE: A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. MATERIALS AND METHODS: We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). RESULTS: SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. CONCLUSION: Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
Files in This Item:
T201303661.pdf Download
DOI
10.3349/ymj.2013.54.6.1321
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Preventive Medicine and Public Health (예방의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
Park, Eun-Cheol(박은철) ORCID logo https://orcid.org/0000-0002-2306-5398
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/88269
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