• Subhashini R
• M K Jeyakumar

Almost 10% of the population in the worldwide is affected with a major health problem chronic kidney disease. However, the prediction of chronic kidney diseases is evidently done using systematic and automatic methodologies. Among the methodologies, the machine learning is one of the very kinds. The classifier in the machine learning algorithms can provide the class labels to the test samples with known features and unknown class. The existing works with machine learning algorithms fail to provide the accuracy of prediction to the needed extent. In order to fulfil the gap, this paper proposes a novel classification strategy to predict the chronic kidney disease from the medical dataset with environmental factors. The methodology introduced in this article is Optimal Fuzzy-K nearest neighbour technique. The optimum performance of fuzzy is obtained by tuning the membership functions utilizing the Bat optimization algorithm. Then the OF is utilised to measure the similarity in the KNN for the classification of disease. The performance of the proposed technique is analysed by the comparison with conventional methods in terms of with and without environmental factors. The accuracy is considered as the primary metric to evaluate the performance, and it is proved the proposed method provides better classification accuracy.

Chronic kidney diseases, bat algorithm, fuzzy technique, K- Nearest Neighbour, training-testing samples, triangular membership function, environmental factors

"Hybrid Algorithm for Chronic Kidney Disease Prediction with Environmental Factors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.100-109, January-2019, Available :http://www.jetir.org/papers/JETIR1901814.pdf

"Hybrid Algorithm for Chronic Kidney Disease Prediction with Environmental Factors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp100-109, January-2019, Available at : http://www.jetir.org/papers/JETIR1901814.pdf