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APPLICATION OF GENETIC ALGORITHM OPTIMIZED NEURAL NETWORK CONNECTION WEIGHTS FOR MEDICAL DIAGNOSIS OF PIMA INDIANS DIABETES

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Author(s): Asha Gowda Karegowda | A.S. Manjunath | M.A. Jayaram

Journal: International Journal on Soft Computing
ISSN 2229-7103

Volume: 2;
Issue: 2;
Start page: 15;
Date: 2011;
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Keywords: Back Propagation Network | Genetic algorithm | connection weight optimisation

ABSTRACT
Neural Networks are one of many data mining analytical tools that can be utilized to make predictions formedical data. Model selection for a neural network entails various factors such as selection of the optimalnumber of hidden nodes, selection of the relevant input variables and selection of optimal connectionweights. This paper presents the application of hybrid model that integrates Genetic Algorithm and BackPropatation network(BPN) where GA is used to initialize and optmize the connection weights of BPN .Significant feactures identified by using two methods :Decision tree and GA-CFS method are used asinput to the hybrid model to diagonise diabetes mellitus. The results prove that, GA-optimized BPNapproach has outperformed the BPN approach without GA optimization. In addition the hybrid GA-BPNwith relevant inputs lead to further improvised categorization accuracy compared to results produced byGA-BPN alone with some redundant inputs.
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