An Automated Diagnostic System for Heart Disease Prediction Based on ${\chi^{2}}$ Statistical Model and Optimally Configured Deep Neural Network

Different automated decision support systems based on artificial neural network (ANN) have been widely proposed for the detection of heart disease in previous studies. However, most of these techniques focus on the preprocessing of features only. In this paper, we focus on both, i.e., refinement of features and elimination of the problems posed by the predictive model, i.e., the problems of underfitting and overfitting. By avoiding the model from overfitting and underfitting, it can show good performance on both the datasets, i.e., training data and testing data. Inappropriate network configuration and irrelevant features often result in overfitting the training data. To eliminate irrelevant features, we propose to use $\chi ^{2}$ statistical model while the optimally configured deep neural network (DNN) is searched by using exhaustive search strategy. The strength of the proposed hybrid model named $\chi ^{2}$ -DNN is evaluated by comparing its performance with conventional ANN and DNN models, another state of the art machine learning models and previously reported methods for heart disease prediction. The proposed model achieves the prediction accuracy of 93.33%. The obtained results are promising compared to the previously reported methods. The findings of the study suggest that the proposed diagnostic system can be used by physicians to accurately predict heart disease.