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

DC Field Value Language
dc.contributor.author김덕원-
dc.contributor.author박은철-
dc.date.accessioned2014-12-18T09:29:32Z-
dc.date.available2014-12-18T09:29:32Z-
dc.date.issued2013-
dc.identifier.issn0513-5796-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/88269-
dc.description.abstractPURPOSE: 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.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfYONSEI MEDICAL JOURNAL-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHAged-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHBone Density/physiology*-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMiddle Aged-
dc.subject.MESHOsteoporosis, Postmenopausal-
dc.titleOsteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Preventive Medicine (예방의학)-
dc.contributor.googleauthorTae Keun Yoo-
dc.contributor.googleauthorSung Kean Kim-
dc.contributor.googleauthorDeok Won Kim-
dc.contributor.googleauthorJoon Yul Choi-
dc.contributor.googleauthorWan Hyung Lee-
dc.contributor.googleauthorEin Oh-
dc.contributor.googleauthorEun-Cheol Park-
dc.identifier.doi10.3349/ymj.2013.54.6.1321-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA00376-
dc.contributor.localIdA01618-
dc.relation.journalcodeJ02813-
dc.identifier.eissn1976-2437-
dc.identifier.pmid24142634-
dc.subject.keywordScreening-
dc.subject.keywordclinical decision tools-
dc.subject.keywordmachine learning-
dc.subject.keywordrisk assessment-
dc.subject.keywordsupport vector machines-
dc.contributor.alternativeNameKim, Deok Won-
dc.contributor.alternativeNamePark, Eun Chul-
dc.contributor.affiliatedAuthorKim, Deok Won-
dc.contributor.affiliatedAuthorPark, Eun Chul-
dc.rights.accessRightsfree-
dc.citation.volume54-
dc.citation.number6-
dc.citation.startPage1321-
dc.citation.endPage1330-
dc.identifier.bibliographicCitationYONSEI MEDICAL JOURNAL, Vol.54(6) : 1321-1330, 2013-
dc.identifier.rimsid33170-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Preventive Medicine (예방의학교실) > 1. Journal Papers

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