In the recent days, the prediction models of chronic kidney disease (CKD) becomes significant in the area of decision making which is helpful in healthcare systems. Because of large amount of medical data, efficient models are required to obtain precise results and data classification algorithms can be employed to detect the presence of CKD. Recently, various machine learning (ML) dependent on data classifier technique is presented for forecasting CKD. Since numerous classification algorithms

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... r CKD prediction exist, there is a need to investigate the prediction performance of these algorithms. This paper propose a comparative analysis of 4 data classifier technique such as deep learning (DL), decision tree (DT), random forest (RF) and random tree (RT). The process of classification technique is analyzed with the help of reputed CKD dataset attained from UCI repository. From the simulation outcomes, it is evident that the DL method achieved optimal classifier action with respect to various namely accuracy, precision and recall.