Relative Merits of Data Mining Algorithms of Chronic Kidney Diseases

Abstract

Early prediction of Chronic Kidney Disease in human subjects is considered to be a critical factor for diagnosis and treatment. The use of data mining algorithms to reveal the hidden information from clinical and laboratory samples helps physician in early diagnosis, thus contributing towards increase in accuracy, prediction and detection of Chronic Kidney Disease. The experimental results obtained from this work, with subjected to optimal data mining algorithms for better classification and prediction, of Chronic Kidney Disease. The result of applying relevant algorithms, like K-Nearest Neighbors, Support Vector Machine, Multi Layer Perceptron, Random Forest, are studied for both clinical and laboratory samples. Our findings show that K - Nearest Neighbour algorithm provides the best classification for clinical data and, similarly, Random Forest for laboratory samples, when compared with the performance parameters like, precision, accuracy, recall and F1 Score of other data mining analysis techniques.