Efficient Binary Classifier for Prediction of Diabetes Using Data Preprocessing and Support Vector Machine

Diabetes offer a sea of opportunity to build classifier as wealth of patient data is available in public domain. It is a disease which affects the vast population and hence cost a great deal of money. It spreads over the years to the other organs in body thus make its impact lethal. Thus, the physicians are interested in early and accurate detection of diabetes. This paper presents an efficient binary classifier for detection of diabetes using data preprocessing and Support Vector Machine (SVM). In this study, attribute evaluator and the best first search is used for reducing the number of features. The dimension of the input feature is reduced from eight to three. The dataset used is Pima diabetic dataset from UCI repository. The substantial increase is noted in accuracy by using the data pre processing.