Medical Dataset Classification Using k-NN and Genetic Algorithm

This paper proposes a hybrid technique that applies artificial bee colony (ABC) algorithm for the feature selection and combined k-nearest neighbor (k-NN) with genetic algorithm (GA) used for effective classification. The aim of this paper was to select the finest features including the elimination of the insignificant features of the datasets that severely affect the classification accuracy. The proposed approach used in heart disease and diabetes diagnosis, which has higher impact rate on reducing quality of life throughout the world, is developed. The datasets including heart disease, diabetes, and hepatitis are taken from UCI repository and evaluated by the proposed technique. The classification accuracy is achieved by 10-fold cross-validation. Experimental results show the higher accuracy of our proposed algorithm compared to other existing systems.