PREDICTION OF DIABETIC SYMPTOMS IN PATIENTS USING DATA MINING TECHNIQUES

This paper focuses on the classification of algorithms for identifying the presence of diabetes in patients using data mining techniques. Data-mining is used to predict the pattern from large databases. Data-mining includes data acquisition, data integration, data exploration, model building, and model validation. In this paper different classification algorithms were used to predict the signs of diabetes according to the norms of World Health Organization. The dataset consists of 768 instances and 8 attributes along with class attribute. To predict the presence of diabetes classification algorithms such as BayesNet, Naivebayes, J48 andIBK were used. The result shows that Naïvebayes algorithm works well compared to other techniques.