Abstract: The automated detection of diseases using Machine Learning Techniques has become a key research area lately. Although the computational complexity involved in analyzing a huge data set can be extremely high, nonetheless the merits of getting a desired result surely counts for the complexity involved in the task. In this paper we adopt the K-Means Clustering Algorithm with a single mean vector of centroids, to classify and make clusters of varying probability of likeliness of suspect being prone to CKD. The results are obtained from a Real Case Data-Set from UCI Machine Learning Repository.