Risk prediction of femoral neck osteoporosis using machine learning and conventional methods

Authors:
Tae Keun Yoo;Sung Kean Kim;Ein Oh;Deok Won Kim
Affiliations:
Department of Medicine, Yonsei University College of Medicine, Korea;Department of Medical Engineering, Yonsei University College of Medicine, Korea,Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea;Department of Medicine, Yonsei University College of Medicine, Korea;Department of Medical Engineering, Yonsei University College of Medicine, Korea,Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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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.