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Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods

Authors
 김덕원 
Citation
 Lecture Notes in Computer Science, Vol.7903(2) : 181~188, 2013 
Journal Title
 Lecture Notes in Computer Science 
ISSN
 0302-9743 
Issue Date
2013
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.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/87052
DOI
10.1007/978-3-642-38682-4_21
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Medical Engineering
Yonsei Authors
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Link
 http://link.springer.com/chapter/10.1007%2F978-3-642-38682-4_21
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