Abstract
A medical database of 589 women thought to have osteoporosis has been analyzed. A hybrid algorithm consisting of Artificial Neural Networks and Genetic Algorithms was used for the assessment of osteoporosis. Osteoporosis is a common disease, especially in women, and a timely and accurate diagnosis is important for avoiding fractures. In this paper, the 33 initial osteoporosis risk factors are reduced to only 2 risk factors by the proposed hybrid algorithm. That leads to faster data analysis procedures and more accurate diagnostic results. The proposed method may be used as a screening tool that assists surgeons in making an osteoporosis diagnosis.
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Papatheocharous, E., Papadopoulos, H., Andreou, A.S.: Feature Selection Techniques for Software Cost Modelling and Estimation: A Comparative Approach. Engineering Intelligent Systems 18(3-4), 233–246 (2010)
Papatheocharous, E., Papadopoulos, H., Andreou, A.S.: Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection. In: Proceedings of the 3rd Workshop on Artificial Intelligence Techniques in Software Engineering, AISEW 2010, CoRR abs/1012.5754 (2010)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer (1996)
Consensus Development Conference: Diagnosis, prophylaxis and treatment of osteoporosis. Am. J. Med. 94, 646–650 (1993)
Cooper, C., Atkinson, E.J., Jacobsen, S.J., O’Fallon, W.M., Melton, L.J.: A population based study of survival after osteoporotic fractures. Am. J. Epidemiol. 137, 1001–1005 (1993)
Johnell, O., Kanis, J.A.: An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 17, 1726–1733 (2006)
Kanis, J.A., Burlet, N., Cooper, C., Delmas, P.D., Reginster, J.Y., Borgstrom, F., Rizzoli, R.: European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO). European Guidance for the Diagnosis and Management of Osteoporosis in Postmenopausal Women. Osteoporos Int. 19(4), 399–428 (2008)
World Health Organization: Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Technical Report Series 843. WHO, Geneva (1994)
Blake, G.M., Fogelman, I.: Role of dual-energy X-ray absorptiometry in the diagnosis and treatment of osteoporosis. J. Clin. Densitom. 10, 102–110 (2007)
Engelke, K., Gluer, C.C.: Quality and performance measures in bone densitometry. I. Errors and diagnosis. Osteoporos Int. 17, 1283–1292 (2006)
Marshall, D., Johnell, O., Wedel, H.: Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. Br. Med. J. 312, 1254–1259 (1996)
Michaëlsson, K., Bergström, R., Mallmin, H., Holmberg, L., Wolk, A., Ljunghall, S.: Screening for osteopenia and osteoporosis: selection by body composition. Osteoporos Int. 6(2), 120–126 (1996)
Lydick, E., Cook, K., Turpin, J., Melton, M., Stine, R., Byrnes, C.: Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am. J. Manag. Care. 4(1), 37–48 (1998)
Cadarette, S.M., Jaglal, S.B., Kreiger, N., McIsaac, W.J., Darlington, G.A., Tu, J.V.: Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ 162(9), 1289–1294 (2000)
Sedrine, W.B., Chevallier, T., Zegels, B., Kvasz, A., Micheletti, M.C., Gelas, B., Reginster, J.Y.: Development and assessment of the Osteoporosis Index of Risk (OSIRIS) to facilitate selection of women for bone densitometry. Gynecol. Endocrinol. 16(3), 245–250 (2002)
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.: Osteoporosis Self-Assessment Tool for Asians (OSTA) Research Group. A Simple Tool to Identify Asian Women at Increased Risk of Osteoporosis. Osteoporos Int. 12(8), 699–705 (2001)
Geusens, P., Hochberg, M.C., van der Voort, D.J., Pols, H., van der Klift, M., Siris, E., Melton, M.E., Turpin, J., Byrnes, C., Ross, P.: Performance of risk indices for identifying low bone density in postmenopausal women. Mayo. Clin. Proc. 77(7), 629–637 (2002)
Weinstein, L., Ullery, B.: Identification of at-risk women for osteoporosis screening. Am. J. Obstet. Gynecol. 183(3), 547–549 (2000)
McLeod, K.M., Johnson, C.S.: Identifying Women with Low Bone Mass: A Systematic Review of Screening Tools. Geriatric Nursing 30(3), 164–173 (2009)
Anastassopoulos, G., Mantzaris, D., Iliadis, L., Kazakos, K., Papadopoulos, H.: Osteoporosis Risk Factor Estimation Using Artificial Neural Networks. Engineering Intelligent Systems 18(3/4), 205–211 (2010)
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Anastassopoulos, G.C., Adamopoulos, A., Drosos, G., Kazakos, K., Papadopoulos, H. (2013). Diagnostic Feature Extraction on Osteoporosis Clinical Data Using Genetic Algorithms. In: Papadopoulos, H., Andreou, A.S., Iliadis, L., Maglogiannis, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2013. IFIP Advances in Information and Communication Technology, vol 412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41142-7_31
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DOI: https://doi.org/10.1007/978-3-642-41142-7_31
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