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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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George C. Anastassopoulos, Adam Adamopoulos, Georgios Drosos, Konstantinos Kazakos, Harris Papadopoulos. Diagnostic Feature Extraction on Osteoporosis Clinical Data Using Genetic Algorithms. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. pp.302-310, ⟨10.1007/978-3-642-41142-7_31⟩. ⟨hal-01459626⟩



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