Osteoporosis risk prediction using machine learning and conventional methods

• Published in: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 188 - 191
• Main Authors: Kim, Sung Kean, Yoo, Tae Keun, Oh, Ein, Kim, Deok Won
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2013
• Subjects: Accuracy; Aged; Area Under Curve; Artificial Intelligence; Artificial neural networks; Bone and Bones - physiopathology; Bone Density; Demography; Female; Humans; Learning systems; Logistic Models; Middle Aged; Neural Networks (Computer); Osteoporosis; Osteoporosis, Postmenopausal - diagnosis; Predictive models; Radio frequency; Republic of Korea; Risk Assessment - methods; ROC Curve; Support Vector Machine; Support vector machines; Republic of Korea
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2013.6609469

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, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.