HPCC: An ensembled framework for the prediction of the onset of diabetes

Diagnosing Diabetes is one of the problems that require high level of accurate analysis and prediction. Traditional techniques for clinical decision support systems are grounded on a single classifier or combination of various classifiers which are used for the diagnosis of the disease and its prediction. Recently much heed has been paid to improve the performance of disease prediction with the use of ensemble-based methods. Using ensemble methods in decision support systems assist in analyzing theses type of diseases. To improve the performance of weak classifiers boosting and bagging can be used. These techniques are based on combining the outputs and functionality of the classifiers. A weighted majority vote or a simple majority vote which has been used in this study are the most common rules for bagging and boosting. In this paper, we compare the performance of bagging and boosting with our hybrid approach called Hierarchical and Progressive Combination of Classifiers (HPCC) through the study of the famous Pima Indians Diabetes Dataset and the best classifier is chosen on the basis of the accuracy achieved.