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.

冠状动脉疾病（CAD）会导致严重问题，例如患有心血管疾病的患者出现严重的心脏病发作，心力衰竭和心绞痛。通过了解重要症状并在早期诊断疾病，可以预防这些疾病。为了诊断CAD，临床医生经常使用血管造影，但是，这是一种侵入性手术，会导致高昂的费用并引起严重的副作用。因此，其他替代方案（例如数据挖掘和机器学习技术）已得到广泛应用。因此，本文提出了一种最新的高精度机器学习模型情绪神经网络（EmNNs）的开发方法，该模型与常规粒子群优化（PSO）技术混合用于诊断CAD。为了提高建议模型的性能，本文在Z-Alizadeh sani数据集上采用了四种不同的特征选择方法，即Fisher，Relief-F，最小冗余最大相关性和SVM权重。除了常规的权重和偏见之外，EmNN还使用情感参数来增强网络的学习能力。此外，将所提出模型的效率与基于PSO的自适应神经模糊推理系统（PSO-ANFIS）进行了比较。发现该模型优于PSO-ANFIS模型。在所有10倍交叉验证中获得的最高准确度，精密度，灵敏度，特异性和F1评分平均值分别为88.34％，92.37％，91.85％，78.98％和92.12％，与已知产品相比具有竞争力文献中的方法。