According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.

据世界卫生组织称，欧盟 50% 的死亡是由心血管疾病 (CVD) 引起的，而 80% 的过早心脏病和中风是可以预防的。在这项研究中，提出了一种用于精确诊断冠状动脉疾病 (CAD) 的计算机辅助诊断模型。该方法基于状态空间高级模糊认知图 (AFCM)，这是传统模糊认知图的演变。此外，还纳入了基于规则的机制，以进一步增加对拟议系统的了解和决策机制的可解释性。所提出的方法是利用希腊帕特雷大学医院核医学系的 CAD 数据集进行评估的。进行了几个实验来定义所提出的 AFCM 的最佳参数。此外，所提出的 AFCM 与传统的 FCM 方法和文献进行了比较。实验突出了 AFCM 方法的有效性，在 CAD 诊断中获得了 85.47% 的准确率，比传统方法提高了 +7%。结果表明，AFCM 方法在开发模糊认知图方面优于传统方法，同时它构成了诊断冠状动脉疾病的可靠方法。