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.
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References
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151(1-2), 155–176 (2003)
De Cos Juez, F.J., Suárez-Suárez, M.A., Sánchez Lasheras, F., Murcia-Mazón, A.: Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Math. Comput. Model. 54(7-8), 1665–1670 (2011)
Dreiseitl, S., Ohno-Machado, L.: Logistic regression and artificial neural network classification models: a methodology review. J. Biomed. Inform. 35(5-6), 352–359 (2002)
Hsieh, C.H., Lu, R.H., Lee, N.H., Chiu, N.T., Hsu, M.H., Li, Y.C.: Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery 149(1), 87–93 (2011)
Kanis, J.A.: Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. Osteoporos. Int. 4(6), 368–381 (1994)
Khosla, S., Melton III, L.J.: Osteopenia. N. Engl. J. Med. 356, 2293–2300 (2007)
Koh, L.K., Sedrine, W.B., Torralba, T.P., Kung, A., Fujiwara, S., Chan, S.P., Huang, Q.R., Rajatanavin, R., Tsai, K.S., Park, H.M., Reginster, J.Y.: A simple tool to identify Asian women at increased risk of osteoporosis. Osteoporos. Int. 12(8), 699–705 (2001)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995)
Korean National Statistical Office (2010), http://www.kosis.kr
Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Handling imbalanced datasets: A review. GESTS Int. T. Comput. Sci. Eng. 30, 25–36 (2006)
Lix, L.M., Yogendran, M.S., Leslie, W.D., Shaw, S.Y., Baumgartner, R., Bowman, C., Metge, C., Gumel, A., Hux, J., James, R.C.: Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases. J. Clin. Epidemiol. 61(12), 1250–1260 (2008)
Marshall, D., Johnell, O., Wedel, H.: Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312(7041), 1254–1259 (1996)
Nayak, S., Roberts, M.S., Greenspan, S.L.: Cost-effectiveness of different screening strategies for osteoporosis in postmenopausal women. Ann. Intern. Med. 155(11), 751–761 (2011)
Ordóñez, C., Matías, J.M., de Cos Juez, J.F., García, P.J.: Machine learning techniques applied to the determination of osteoporosis incidence in post-menopausal women. Math. Comput. Model. 50(5-6), 673–679 (2009)
Raisz, L.G.: Screening for osteoporosis. N. Engl. J. Med. 353, 164–171 (2005)
Richy, F., Gourlay, M., Ross, P.D., Sen, S.S., Radican, L., Ceulaer, F.D., Sedrine, W.B., Ethgen, O., Bruyere, O., Reginster, J.Y.: Validation and comparative evaluation of the osteoporosis self-assessment tool (OST) in a Caucasian population from Belgium. QJM 97(1), 39–46 (2004)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Sun, Y., Kamel, M.S., Wong, A.K.C., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)
Walid, M., Ahmad, S., Fadi, C., Dima, R.: Intelligent predictive osteoporosis system. Int. J. Comput. Appl. 32, 28–30 (2011)
Yeun, E.J.: A study on the health promoting lifestyle practices of middle-aged women in Korea. J. Korean Soc. Health Educ. Promo. 17(1), 41–59 (2000)
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Yoo, T.K., Kim, S.K., Oh, E., Kim, D.W. (2013). Risk Prediction of Femoral Neck Osteoporosis Using Machine Learning and Conventional Methods. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_21
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DOI: https://doi.org/10.1007/978-3-642-38682-4_21
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