Diagnostic Application of DNNS to Coronary Heart Disease
Keywords:
Accuracy, cardiovascular disease, classification, coronary artery disease, diagnosis, heart disease.Abstract
According to the World Health Organization (WHO), CVD is the leading cause of mortality globally. In 2015, CVD was responsible for more than 75% deaths that occurred in worldwide. In US, due to heart disease approximately 630,000 fatalities annually, accounting for 25% of all deaths. In 2015, coronary heart disease was the top cause of mortality in the US, claiming the lives of over 360,000 Americans. This information underscores the importance of addressing cardiovascular disease as a major global health concern and highlights the potential of advanced technology, such as deep neural network learning, to aid in the early detection and prediction of coronary heart disease, ultimately benefiting healthcare on a global scale. The DNN model utilized regularization, dropout techniques, and an improved multilayer perception architecture. DNN learning model achieved impressive performance metrics viz. F-score: 0.9571, area under the ROC curve: 0.9812, Kolmogorov-Smirnov (K-S) test: 67.62, diagnostic odds ratio: 39.75, 95% confidence interval: [38.65, 110.28], accuracy: 84.67%, sensitivity: 94.51%, specificity: 73.86%, precision: 80.12%. A dataset containing 303 clinical occurrences was used for training the model. The application of such models can contribute to improving public health and global health outcomes. These models have the potential to assist healthcare professionals and patients worldwide, especially in low-income and resource-constrained settings where cardiac experts are scarce.
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