Classification of Pima Indian Diabetes Dataset using Ensemble of Decision Tree, Logistic Regression and Neural Network

Abstract: This paper proposed an ensemble hierarchical model to combine two or more classifiers which has been trained independently, and then fused them in the next level. This is done in two steps, first we trained a Decision Tree and a Logistic Regression models, step two we feed the output of those models to a Neural Network. The Neural Network is also trained to combine the output of previous classifiers to achieve better overall accuracy. To test our hypothesis, we used PIMA Indian diabetes database as benchmark problem. Our proposed model has achieved classification accuracy above 83% which is better than other states of the art methods in the literature. Keywords: Data mining, Regression, Neural Network, Decision Tree, Pima Diabetes Data set, Ensemble Learning.