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DATA MINING TECHNIQUES FOR THE ANALYSIS OF TYPE-2 DIABETES–A SURVEY

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

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Abstract

Invigouranxiety businesses, data mining plays an important role in early prophecy of diseases. Numerous tests must be conducted in a patient to detect a disease. Data mining techniques is used in disease prediction to reduce the test and increase the accuracy of rate of detection. Diabetes mellitus is one of the most common diseases among adolescent. This develops at a middle age and more common in overweight children and youngsters. so as to reduce the population with diabetes mellitus it should be detected at an earlier stage.This paper explore the survey on forecasting of diabetes using different data mining techniques such as Fuzzy Logic,Naive Bayes,J48 (C4.5), JRip, Neural networks, Decision trees.

References

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Keywords

Diabetes Mellitus,FuzzyLogic,Naive Bayes, J48 (C4.5), JRip, Neural networks, Decision tree.

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  • Format Volume 5, Issue 1, No 18, 2017
  • Copyright All Rights Reserved ©2017
  • Year of Publication 2017
  • Author V.SUDHARSANA, T.REVATHI
  • Reference IJCS-241
  • Page No 1517-1523

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