As the amount of data is increasing, it has become merely impossible to carry out the manual analysis of data. As there is enough variety of data crisp data set is not so practical, hence the need to process the fuzzy data is necessary. Data mining algorithms helps in analyzing and predicting the huge amount of data, with very less human effort. This project aims at implementation of enhanced fuzzy back propagation neural network with triangular membership function and its comparative analysis with neural network, Naive Bayes. The project aims at predicting the states of diabetic. As diabetic is a serious problem and need to be predicted. We have eight indicators which determine the state of the protein. The best algorithm found was Naïve Bayes with an accuracy of 89% followed by fuzzy ANN with 86% accuracy and standard ANN had 82% accuracy. The study was done in java using Java Servlet packages and SQL database for storage. A precise and accurate prediction is necessary as sometimes there can human errors which lead to poor diagnosis of data and sometimes false report, so accurate prediction is required.
