Heart Disease Detection from Gene Expression Data Using Optimization Driven Deep Q-Network

Heart disease is a clinical illness that is caused by structural and functional tissue dysfunction. According to the arena health company (WHO), heart disease is the leading cause of death worldwide, with a recent study finding that heart disease accounts for 23% of all deaths in the United States. As a result, early and accurate diagnosis of heart-related diseases is crucial in improving the health of patients with heart disease and in reducing their mortality by preventing unsafe deaths through better medical services. In-depth learning algorithms are now widely used in medical science to diagnose various diseases. Using basic models to predict heart disease is very difficult due to insufficient genetic representation and certain problems of inequality. This study employs an effective technique for diagnosing heart illness utilizing a deep Q-network based on the suggested political deer hunting optimization (PDHO) algorithm. The proposed PDHO method, on the other hand, is the result of combining the political optimizer (PO) with the deer hunting optimization (DHO) algorithms. In addition, the proposed PDHO-based deep Q-network for heart disease diagnosis had a maximum accuracy of 0.934, sensitivity of 0.962, and specificity of 0.892.