The methods of duo output neural network ensemble for prediction of coronary heart disease

Abstract

The occurrence of Coronary heart disease (CHD) is hard to predict yet , but the assessment of CHD risk for the next ten years is possible . The p rediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN ha s either positive or negative CHD output , which is a binary classification. A prediction model with binary classification requires determin ation of threshold value before the classification process which increases the uncertainty in the classification process. Another weakness of the MLP-ANN model is the presence of overfitting. This study propose s a prediction model for coronary heart disease using the duo output artificial neural network ensemble (DOANNE) method to overcome the problems of overfitting and uncertainty of classification in MLP-ANN. This research method wa s divided into several stages, namely data acquisition, pre-processing, modelling into DOANNE, neural network ensemble training with Levenberg-Marquard (LM) algorithm, system performance testing, and evaluation. The results of the study showed that the use of DOANNE-LM method was able to provide a significant improvement from the MLP-ANN method, indicated by the results of statistical tests with p-value <0.05.