A Hybrid Decision Tree - Artificial Neural Networks Ensemble Approach for Kidney Transplantation Outcomes Prediction

The learning strategy employed in neural networks offers a good performance even in the situations where a model is presented with incomplete and noisy data. However, neural networks are known as 'black boxes' as how the outputs are produced is not clear. In this study, a hybrid learning strategy, namely RDC-ANNE (Rules Driven by Consistency in Artificial Neural Networks Ensemble) is proposed. This paper looks at the use of RDC- ANNE in the graft outcome prediction domain as a prototypical medical application. At first, for a better generalization, a committee of binary neural networks is trained. Then, a partial C4.5 decision tree is built from a specifically selected dataset, generated based on the graft data used to test the trained neural networks ensemble. Finally the most appropriate leaf in every path is converted into an understandable rule. In this approach, for the rule generation process, we enforced the model to mainly consider the patterns that their class labels were consistently causing agreement across the neural network classifiers. Experimental results show that the RDC-ANNE method is able to extract partial rules from an ensemble model and reveal the important embedded information of a trained neural network ensemble.