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Coronary artery disease (CAD) can cause serious conditions such as severe heart attack, heart failure, and angina in patients with cardiovascular problems. These conditions may be prevented by knowing the important symptoms and diagnosing the disease in the early stage. For diagnosing CAD, clinicians often use angiography, however, it is an invasive procedure that incurs high costs and causes severe side effects. Therefore, the other alternatives such as data mining and machine learning techniques have been applied extensively. Accordingly, the paper proposes a recent development of a highly accurate machine learning model emotional neural networks (EmNNs) which is hybridized with conventional particle swarm optimization (PSO) technique for the diagnosis of CAD. To enhance the performance of the proposed model, the paper employs four different feature selection methods, namely Fisher, Relief …