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Severity Estimation & Food Recommendation for Diabetic Patient using Data Mining


Sandeep Gupta , Ideal institute of technology ; Nilima Bhoir, Ideal institute of technology ; Ashish Gupta, Ideal institute of technology ; Trupti Kini


Data Mining, ID3 Algorithm, Asp.Net, Classification, Prediction


This paper analyzes the Diabetes prediction using various DM techniques. Some of the most important and popular data mining techniques are classification, clustering, prediction, Naive Bayes, Decision Tree are analyzed to predict the diabetes disease. Data mining plays an efficient role in prediction of diseases in health care industry. Diabetes is a metabolic disease where the improper management of blood glucose levels led to risk of generating abnormalities in functioning of critical organs like heart, kidney, eye etc. So the patients need to visit their specialist for observing sugar level related risks and sit tight for a day or more to get their conclusion report, they need to squander their cash futile as well. The proposed system calculates severity by using ID3 algorithm, Machine Learning approach based on the input given and at the same time it will recommend what food should be consumed by the patient to avoid health hazards. Data mining techniques can be used for early prediction of the disease with greater quality in order to save the human life and it will also reduce the treatment cost. According to International Diabetes Federation stated that 382 million people are affected with diabetes worldwide.

Other Details

Paper ID: IJSRDV6I120064
Published in: Volume : 6, Issue : 12
Publication Date: 01/03/2019
Page(s): 220-222

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