An efficient pre-processing method for improved classification of diabetics using decision tree and artificial neural network

Abstract

The medical dataset is growing on a daily basis and there is a need to efficiently use this data to assist in the diagnosis of vast multitude of diseases. With the advancement in data mining, it is possible to extract interesting patterns from ever growing datasets in order to help take wise medical decisions. Diabetes is a chronic disease, caused due to either insufficient production of insulin by the pancreas or the cells of the body are not responding properly to the insulin produced. It leads to complications that affects heart, kidney, nerve and blood vessel damage, blindness and hence needs to be diagnosed at an early stage. This research work analyses different pre-processing tasks and identifies the best one that performs better than all the other techniques by constructing various classifier models. The classification model categories whether a person has diabetes or not. The four classification models explored were ANN, ID3, C4.5, and CART; compared the models based on evaluation parameters like Accuracy, Sensitivity, Specificity and Precision.