Cited 1 times in

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

Title
Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods
Authors
Tae Keun Yoo;Sung Kean Kim;Deok Won Kim;Ein Oh
Issue Date
2013
Journal Title
Lecture Notes in Computer Science
ISSN
0302-9743
Citation
Lecture Notes in Computer Science, Vol.7903(2) : 181~188, 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://link.springer.com/chapter/10.1007%2F978-3-642-38682-4_21

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
사서에게 알리기
  feedback
Files in This Item:
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse