Abstract: According to the World Health Organization, cardiovascular disease (CVD) is the top cause of death worldwide. In 2015, over 30% of global deaths was due to CVD, leading to over 17 million deaths, a global health burden. Of those deaths, over 7 million were caused by heart disease, and greater than 75% of deaths due to CVD were in developing countries. In the United States alone, 25% of deaths is attributed to heart disease, killing over 630,000 Americans annually. Among heart disease conditions, coronary heart disease is the most common, causing over 360,000 American deaths due to heart attacks in 2015. Thus, coronary heart disease is a public health issue. In this research paper, an enhanced deep neural network (DNN) learning was developed to aid patients and healthcare professionals and to increase the accuracy and reliability of heart disease diagnosis and prognosis in patients. The developed DNN learning model is based on a deeper multilayer perceptron architecture with regularization and dropout using deep learning. The developed DNN learning model includes a classification model based on training data and a prediction model for diagnosing new patient cases using a data set of 303 clinical instances from patients diagnosed with coronary heart disease at the Cleveland Clinic Foundation. The testing results showed that the DNN classification and prediction model achieved the following results: diagnostic accuracy of 83.67%, sensitivity of 93.51%, specificity of 72.86%, precision of 79.12%, F-Score of 0.8571, area under the ROC curve of 0.8922, Kolmogorov-Smirnov (K-S) test of 66.62%, diagnostic odds ratio (DOR) of 38.65, and 95% confidence interval for the DOR test of [38.65, 110.28]. Therefore, clinical diagnoses of coronary heart disease were reliably and accurately derived from the developed DNN classification and prediction models. Thus, the models can be used to aid healthcare professionals and patients throughout the world to advance both public health and global health, especially in developing countries and resource-limited areas with fewer cardiac specialists available.
Keywords: Cardiovascular disease; heart disease; coronary artery disease; classification; accuracy; diagnosis; diagnostic odds ratio; deep learning; deep neural network; machine learning; F-score; global health; public health; K-S test; precision; prediction; prognosis; ROC curve; specificity; sensitivity