Abstract :
The medical industry incredibly utilizes the data mining systems for different expectations and characterization. The substantial data repositories produced is subjected to different calculations to distinguish the examples in the data. The diabetic is the most undermining ailment with the end goal where millions of people suffers each year. In this paper the forecast of the diabetics is done by utilizing different procedures like classification and prediction techniques decision tree, Naive Bayes, Support vendor machine SVM , clustering, K Nearest Neighbour, K means, K medoids, Neural Networks, Association rule mining and Multilayer Preceptron have been examined broadly. It is seen from the examination that the Naïve Bayes and C4.5 algorithm system show to have better execution with satisfactory results. T. Padma Nivethitha | M. Uma Maheswari | Dr. J. G. R. Sathiaseelan "A Survey on Classification and Prediction Techniques in Data Mining for Diabetes Mellitus" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15878.pdf Paper URL: http://www.ijtsrd.com/computer-science/data-miining/15878/a-survey-on-classification-and-prediction-techniques-in-data-mining-for-diabetes-mellitus/t-padma-nivethitha
Keyword :
Data Mining, Diabetes, Prediction, C4.5, Naïve Bayes.