Abstract

The Chronic Kidney disease is the most important health issues concerning the people as a whole. Chronic diseases lead to morbidity and increase of death rates in India and other low and middle income countries. The chronic diseases account to about 60% of all deaths worldwide. 80% of chronic disease deaths worldwide also occur in low and middle income countries. In India, the number of deaths due to chronic disease found to be 5.21 million in 2008 and seems to be raised to 7.63 million in 2020 approx 66.7% .In Chronic Kidney Disease dataset contain 24 features and achieved Accuracy. In this paper, a binary Grey Wolf Optimization algorithm is used for feature selection and compared with PSO-KDE and GA-KDE where PSO-KDE model is proposed that hybridize the particle swarm optimization (PSO) and kernel density estimation (KDE) based classifier to diagnosis of chronic kidney disease. Classification performance and the number of selected features are the criteria used to design the objective function of PSO-KDE and GA-KDE. The Experimental results prove that the PSO-KDE model has better average performance in diagnosis of kidney disease.

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