Coronary artery disease (CAD) is one of the most lethal diseases which is major cause of deaths around the globe. CAD is among such diseases with mortality rate approximately 7 million per annum. Though, early detection, prognostication and timely diagnosis can help in mortality rate reduction. Conventional CAD detection systems are cumbersome and expensive. Moreover, scarcity or uneven distribution of radiologists in different geographical locations is a hindrance in early diagnosis. Therefore, this is the time when researchers and doctors are collaboratively looking forward for developing a computational intelligence system in the area of medical imaging systems for prognostication, identification, treatment and disease diagnosis. To support the vision of researchers, a computational intelligence system for coronary artery disease diagnosis, C-CADZ, has been proposed. To validate the model, C-CADZ, the dataset namely, Z-Alizadeh Sani CAD dataset from UCI repository is considered. C-CADZ utilizes the fixed analysis of mixed data (FAMD) for feature extraction. FAMD extracts 96 features. In order to retrieve significant features, nature-inspired algorithms are utilized. C-CADZ implemented Synthetic Minority Oversampling Technique (SMOTE) to handle class-imbalanced data as machine learning (ML) predictive models are built to handle class-balanced datasets. Z-Score normalization technique is used for normalizing the dataset. Furthermore, C-CADZ is trained using ML classifiers, Random Forest (RF) and Extra Trees (ET) and validated using holdout validation scheme with hold-out ratio 3 : 1. Experimentation results show that C-CADZ outperforms state-of-the-art methods of last decades in terms of accuracy. C-CADZ has gained an increase in accuracy from state-of-the-art methods published in 2020 by 5.17% with performance metric 〈Acc, Sens, Spec〉≡〈97.37, 98.15, 95.45〉. The performance analysis shows that achieving highest accuracy and the stable nature of boxplot and ROC-AUC curve of RF-ET makes it suitable for heart disease prediction.

冠状动脉疾病 (CAD) 是最致命的疾病之一，是全球死亡的主要原因。CAD 属于此类疾病，每年死亡率约为 700 万。尽管如此，早期发现、预测和及时诊断有助于降低死亡率。传统的 CAD 检测系统既笨重又昂贵。此外，不同地理位置的放射科医生稀缺或分布不均是早期诊断的障碍。因此，这是研究人员和医生共同期待在医学成像系统领域开发用于预测、识别、治疗和疾病诊断的计算智能系统的时候。为了支持研究人员的愿景，用于冠状动脉疾病诊断的计算智能系统，C-CADZ，已被提出。为了验证模型 C-CADZ，考虑了数据集，即来自 UCI 存储库的 Z-Alizadeh Sani CAD 数据集。C-CADZ 利用混合数据的固定分析 (FAMD) 进行特征提取。FAMD 提取了 96 个特征。为了检索重要特征，使用了受自然启发的算法。C-CADZ 实施了合成少数过采样技术 (SMOTE) 来处理类不平衡数据，因为构建了机器学习 (ML) 预测模型来处理类平衡数据集。Z-Score 归一化技术用于归一化数据集。此外，C-CADZ 使用 ML 分类器、随机森林 (RF) 和额外树 (ET) 进行训练，并使用保留比为 3:1 的保留验证方案进行验证。实验结果表明，C-CADZ 在准确性方面优于过去几十年的最先进方法。C-CADZ 从 2020 年发布的最先进方法中获得了 5.17% 的准确度提高，性能指标<A c c , S e n s , S p e c〉≡〈97.37, 98.15, 95.45〉。性能分析表明，RF-ET 的箱线图和 ROC-AUC 曲线的最高准确度和稳定性使其适用于心脏病预测。