alexa A fast and adaptive automated disease diagnosis method with an innovative neural network model.
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Alkm E, Grbz E, Kl E

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Abstract Automatic disease diagnosis systems have been used for many years. While these systems are constructed, the data used needs to be classified appropriately. For this purpose, a variety of methods have been proposed in the literature so far. As distinct from the ones in the literature, in this study, a general-purpose, fast and adaptive disease diagnosis system is developed. This newly proposed method is based on Learning Vector Quantization (LVQ) artificial neural networks which are powerful classification algorithms. In this study, the classification ability of LVQ networks is developed by embedding a reinforcement mechanism into the LVQ network in order to increase the success rate of the disease diagnosis method and reduce the decision time. The parameters of the reinforcement learning mechanism are updated in an adaptive way in the network. Thus, the loss of time due to incorrect selection of the parameters and decrement in the success rate are avoided. After the development process mentioned, the newly proposed classification technique is named "Adaptive LVQ with Reinforcement Mechanism (ALVQ-RM)". The method proposed handles data with missing values. To prove that this method did not offer a special solution for a particular disease, because of its adaptive structure, it is used both for diagnosis of breast cancer, and for diagnosis of thyroid disorders, and a correct diagnosis rate after replacing missing values using median method over 99.5\% is acquired in average for both diseases. In addition, the success rate of determination of the parameters of the proposed "LVQ with Reinforcement Mechanism (LVQ-RM)" classifier, and how this determination affected the required number of iterations for acquiring that success rate are discussed with comparison to the other studies. Copyright © 2012 Elsevier Ltd. All rights reserved. This article was published in Neural Netw and referenced in International Journal of Biomedical Data Mining

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