Paper Title:Prediction of diabetes using Classification algorithms

Abstract

The main objective of the research is to predict the diabetes patient and Normal patient based on test results or test reports using classification algorithms. In Data mining, different techniques can be used for solving problems. For example, classification, prediction, clustering are data mining techniques. Classification is the process of classify the data according to the features of the data with predefined set of classes. Prediction is Used for predicting the class label for new data. The weka tool is used to develop a classifier for predicting the diabetes patient and normal patient. The Diabetes dataset is used for prediction process. The data set can be divided into two subsets. The first one is training set and other one is test set. The training Set contains set of attributes with class labels. The test set contains set of attributes and it doesn’t contain class labels. It was predicted by classifier or model. The research takes three algorithms such as Naive Bayes, Multilayer Perceptron and IBK. Each algorithm provides best accuracy for prediction process. The accuracy of the Naive Bayes algorithm is 100%.


Keywords:Diabetes patient, Normal patient, Prediction, Accuracy and Classifiers.