Improving Efficiency in Diabetes Detection Using Neural Network Technique
Abstract: Diabetes mellitus is an interminable disease that forces excessively high human, social and financial expenses for a nation. Additionally, minimizing its commonness rate and in addition its excessive and risky confusions requires viable administration. This paper is an effort to plan and execute a descriptive data mining approach and to devise association standards to predict diabetes behavior in arrangement with particular life style parameters, including physical activity and emotional states, especially in elderly diabetics using Neural Network. In our work network classifier has been used with different test parameters and it was found that it is effective in diagnosis of Diabetes mellitus when the person provide the required attributes value. The dataset was taken from diabetes database Indian PIMA from the UCI Machine Learning Database. The dataset is comprise of eight features which are vital in diagnosis for diabetes detection. The System is model on multilayer neural network trained with back-propagation and simulated on feed-forward neural network.

Authors: Vivek Vaidya, Dr. L. K. Vishwamitra

File Name: Vivek_90200-20-101.pdf
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