Classification of Chronic Kidney Disease Data via Three Algorithms

Abstract

Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns. The classification of objects is an important area for research and application in a variety of fields. In this paper, k-Nearest Neighbor, Fuzzy k-Nearest Neighbor and Modified k-Nearest Neighbor algorithms are used to classify of the chronic kidney disease (CKD) data with different choices of value k. The experiment results prove that the Fuzzy k-Nearest Neighbor and Modified k-Nearest Neighbor algorithms are very effective for classifying CKD data with high classification accuracy.