A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network

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-F, Minimum Redundancy Maximum Relevance, and Weight by SVM, on Z-Alizadeh sani dataset. The EmNNs, with addition to the conventional weights and biases, uses emotional parameters to enhance the learning ability of the network. Further, the efficiency of the proposed model is compared with the PSO based adaptive neuro-fuzzy inference system (PSO-ANFIS). The proposed model is found better than the PSO-ANFIS model. The obtained highest average values of accuracy, precision, sensitivity, specificity, and F1-score over all the 10-fold cross-validation are 88.34%, 92.37%, 91.85%, 78.98%, and 92.12% respectively which is competitive to the known approaches in the literature. The F1-score obtained by the proposed model over Z-Alizadeh sani dataset is second best among the existing works.