Diabetes Forecasting Using Supervised Learning Techniques

Diabetes Mellitus is one of the most serious health challengesaffecting children, adolescents and young adults in bothdeveloping and developed countries. To predict hidden patternsof diseases diagnostic in the healthcare sector, nowadays we usevarious data mining techniques. In this paper, we have appliedsupervised machine learning techniques like Naive Bayes andJ48 decision tree to identify diabetic patients. We evaluated theproposed methods on Pima Indian diabetes data sets, which is adata mining data sets from UCI machine learning laboratory. Ithas been observed through analysis of the experimental resultsthat Naive Bayes performs better than the decision tree methodJ48.

Diabetes Forecasting Using Supervised Learning Techniques Keywords:

Data Mining , Naive Bayes , J48 , Neural Network , Diabetes , MRBF , RBF , CVD , CHD , ROC , SVM , KNN