Bat imperialist competitive algorithm (BICA) based feature selection and genetic fuzzy based improved kernel support vector machine (GF-IKSVM) classifier for diagnosis of cardiovascular heart disease

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 prediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN has either positive or negative CHD output, which is a binary classification. A prediction model with binary classification requires determination 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 proposes 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 was 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 pvalue <0.05.

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

Wiharto

Suryani

Cahyawati

2019

IJEEI

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Bat imperialist competitive algorithm (BICA) based feature selection and genetic fuzzy based improved kernel support vector machine (GF-IKSVM) classifier for diagnosis of cardiovascular heart disease

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

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

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