Cited 3 times in
Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김덕원 | - |
dc.date.accessioned | 2014-12-18T08:50:34Z | - |
dc.date.available | 2014-12-18T08:50:34Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/87052 | - |
dc.description.abstract | Screening femoral neck osteoporosis is important to prevent fractures of the femoral neck. We developed machine learning models with the aim of more accurately identifying the risk of femoral neck osteoporosis in postmenopausal women and compared those to a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records based on the Korea National Health and Nutrition Surveys. The training set was used to construct models based on popular machine learning algorithms using various predictors associated with osteoporosis. The learning models were compared to OST. Support vector machines (SVM) had better performance than OST. Validation on the test set showed that SVM predicted femoral neck osteoporosis with an area under the curve of the receiver operating characteristic of 0.874, accuracy of 80.4%, sensitivity of 81.3%, and specificity of 80.5%. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis. | - |
dc.description.statementOfResponsibility | open | - |
dc.relation.isPartOf | Lecture Notes in Computer Science | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Bone Density/physiology* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Osteoporosis, Postmenopausal | - |
dc.title | Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Medical Engineering (의학공학) | - |
dc.contributor.googleauthor | Tae Keun Yoo | - |
dc.contributor.googleauthor | Sung Kean Kim | - |
dc.contributor.googleauthor | Ein Oh | - |
dc.contributor.googleauthor | Deok Won Kim | - |
dc.identifier.doi | 10.1007/978-3-642-38682-4_21 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A00376 | - |
dc.relation.journalcode | J02160 | - |
dc.identifier.pmid | 24142634 | - |
dc.identifier.url | http://link.springer.com/chapter/10.1007%2F978-3-642-38682-4_21 | - |
dc.subject.keyword | Screening | - |
dc.subject.keyword | clinical decision tools | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | risk assessment | - |
dc.subject.keyword | support vector machines | - |
dc.contributor.alternativeName | Kim, Deok Won | - |
dc.contributor.affiliatedAuthor | Kim, Deok Won | - |
dc.rights.accessRights | not free | - |
dc.citation.volume | 7903 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 181 | - |
dc.citation.endPage | 188 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science, Vol.7903(2) : 181-188, 2013 | - |
dc.identifier.rimsid | 32127 | - |
dc.type.rims | ART | - |
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