An artificial neural network with bagging to address imbalance datasets on clinical prediction

Class imbalance problem considerably often occurs in real life data setting, particularly in clinical datasets, in which case of a two class classification is not equally presented. This situation causes negative effect on the performance of neural networks that can lead the algorithm to overfit the data and have poor accuracy. Bagging is one of popular ensemble methods that is able to address class imbalance problem. Furthermore, bagging shows well performance with unstable classifiers such as neural networks. The experimental results show that the proposed method, bagging neural networks, has successfully addressed class imbalance problem on clinical diagnosis predictions.