Chronic kidney disease (CKD) is a condition characterized by a gradual loss of kidney function over time. It includes risk of cardiovascular disease and end-stage renal disease. In this paper, we use Machine Learning approach for predicting CKD. In this paper, we present a comparative analysis of seven different machine learning algorithms. This study starts with twenty-four parameters in addition to the class attribute and twenty five percent of the data set is used to test the predictions. Algorithms are trained using fivefold cross-validation and performance of the system is assessed using classification accuracy, confusion matrix, specificity and sensitivity.

CKD, Machine Learning, Logistic Regression, Support Vector Machine, Random Forest

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