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Authors :-S. Kalaiarasi, Chirag A, Mohammed Kaareem Khan R, Ulagappan R, Vignesh M.

Volume/Issue :-
 Volume 3 Issue 4

Google Scholar :-
 https://goo.gl/DF9R4u

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https://goo.gl/C8q7tCY

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Presently most of the people are affected by aperennial disease which is “Diabetes”. A great deal of research is currently being taken place. A model is proposed to foreshow existing system by implementing clustering and classifications techniques which are implemented to determine the type of diabetes. We diagnose diabetes based on the records of patient’s data by which we analyse the seriousness of the diabetes. In this method for clustering the entire dataset into three clusters, where cluster-0 is used for gestational diabetes, cluster-1 is used for type-1 diabetes, cluster-2 is used for type-2 diabetes by using Naïve Bayesian algorithm. The Classification model gives the clustered dataset which further classifies patient’s level of diabetes as mild, moderate and severe. To diagnose diabetes, performance analysis of various algorithms is done. The result is presented by showing the performance of various classification algorithms.
Keywords:- Classification, Sorting, Diagnosis of Diabetes, Naïve Bayes, Random tree.