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Characteristic evaluation of diabetes data using clustering techniques


P.Padmaja, Srikanth Vikkurty, Nilofer Inaz Siddiqui, Praveen Dasari, Bikkina Ambica, V.B.V.E.Venkata Rao, Mastan Vali Shaik, V.J.P. Raju Rudraraju


Vol. 8  No. 11  pp. 244-251


Background & objectives: Taking into account the prevalence of diabetes among women the study is aimed at finding out the characteristics that determine the presence of diabetes and to track the maximum number of women suffering from diabetes. Methods: Data mining functionalities like clustering and attribute oriented induction techniques have been employed to track the characteristics of the women suffering from diabetes. Information related to the study was obtained from National Institute of Diabetes, Digestive and Kidney Diseases. Results: As clustering techniques have been utilized, the results were presented in the form of clusters showing the concentrations of the various attributes and the percentage of women suffering from diabetes with such characteristics. The results were evaluated in five different clusters and they show that 23% of the women suffering from diabetes fall in cluster-0, 5% fall in cluster-1, 23% fall in cluster-2, 8% in cluster-3 and 25% in cluster-3. It was also found that the characteristics seem to be varying for each cluster. Interpretation & Conclusion: From the results it can be interpreted that the characteristics of the women suffering from diabetes are unique with respect to a cluster and no similarity can be found with respect to other clusters. The study helps in predicting the state of diabetes i.e., whether it is in an initial stage or in an advanced stage based on the characteristic results and also helps in estimating the maximum number of women suffering from diabetes with specific characteristics. Thus patients can be given effective treatment by effectively diagnosing the characteristics.


Clustering, diabetes, characteristic evaluation