Diabetes prediction using Hybrid Bagging Classifier

•Diabetes is one of those diseases where the misdiagnosis may cause the heart failure, paralysis, loss of life and more.•Diagnosis of such chronic diseases is essential and should be done carefully as it is the most common disease.•According to the recent statistics in India it is major cause of death.•Hence early prediction of diabetes may decrease the cause of death and increases the survival rate.•Several Machine Learning algorithms were applied for the early detection of diabetes. Diabetes is one of those diseases where the misdiagnosis may cause the heart failure, paralysis, loss of life and more. Diagnosis of such chronic diseases is essential and should be done carefully as it is the most common disease. According to the recent statistics in India it is major cause of death. Hence early prediction of diabetes may decrease the cause of death and increases the survival rate. Several Machine Learning algorithms were applied for the early detection of diabetes. These existing methods face the problems such as time consuming, feature selection not giving good accuracy, specificity and no accurate results. To overcome this problem, the proposed methodology implemented enhanced Recursive Feature Elimination (RFE) for feature selection. Then it classifies the diabetes using Hybrid Bagging Classifier because hybrid approach tunes the parameters of bagging approach according to the dataset.