Data has become an integral part of almost every organization. This data contains interesting and vital information that is often hidden to naked eye but is in the greater interest to an organization, this reason has led researchers for finding a special interest in extracting the hidden knowledge that is accumulated within it, with some researchers terming it as goldmine of data. In this scenario data mining has found a special place in the healthcare sector. Data mining has been found to be quite successful in healthcare sector in finding out the hidden patterns that are useful for disease prognosis. These data mining techniques have been successfully applied for prognosis of diabetes. Diabetes mellitus commonly known as diabetes is a metabolic disorder condition which is characterized by high level of sugar in blood. Numerous data mining techniques have been used for designing of the model that could aid physicians in predicting diabetes. In this paper the main focus is to make present detailed survey of various data mining techniques and approaches that have been put to use for prognosis of diabetes. The research presented here is a survey focused mainly on evaluation of various computer based tools designed for prognosis of diabetes.

Diabetes, Data mining, Decision tree, Dataset, Prognosis, SVM

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