Development of a decision support system for heart disease diagnosis using multilayer perceptron

In this paper, a computational model based on a multilayer perceptron (MLP) neural network with three layers is employed to develop a decision support system for the diagnosis of five major heart diseases. The input layer of the system includes 38 input variables, extracted from a large number of patient cases. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. The proposed decision support system is trained using a back propagation algorithm augmented with the momentum term, the adaptive learning rate and the forgetting mechanics. In addition, the missing data are handled using the substituting mean method. The experimental results have shown that the adopted MLP-based decision model can achieve high accuracy level (63.6-82.9%) on the classification of heart diseases, qualifying it as a good decision support system deployable in clinics.