Determination of osteoporosis risk using by neural networks method

• Main Author: Veysi Akpolat
• Format: Article
• Language: English
• Published: Dicle University Medical School 2009-06-01
• Series: Dicle Medical Journal
• Subjects: Osteoporosis; Artificial neural networks; classification
• Online Access: http://4181.indexcopernicus.com/fulltxt.php?ICID=886061

Artificial neural networks (ANNs) have become modeling tools that have found extensive acceptance and they have frequently used in applications in many disciplines for solving complex problems. Different ANN structures are valuable models, which are used in the medical field for the development of decision support systems. In this paper, the learning and classification processes are used for determining the level of bone-density (safe / risk of osteoporosis) in woman. In this study, three different structured neural networks were used for classifying of osteoporosis and the most efficient structure was determined. The training network structures were Multilayer perceptron neural network (MLP), Linear Vector Quantization (LVQ) and Self Organizing Map (SOM). Performance indicators and statistical measures were used for evaluating the structures and the results demonstrated that the MLP was the most efficient structure for classifying of osteoporosis.