Diagnostic Feature Extraction on Osteoporosis Clinical Data Using Genetic Algorithms

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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Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.302-310, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_31〉
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George Anastassopoulos, Adam Adamopoulos, Georgios Drosos, Konstantinos Kazakos, Harris Papadopoulos. Diagnostic Feature Extraction on Osteoporosis Clinical Data Using Genetic Algorithms. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.302-310, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_31〉. 〈hal-01459626〉

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