Purpose: In this study, the recently emerged advances in Fuzzy Cognitive Maps (FCM) are investigated and employed, for achieving the automatic and non-invasive diagnosis of Coronary Artery Disease (CAD). Methods: A Computer-Aided Diagnostic model for the acceptable and non-invasive prediction of CAD using the State Space Advanced FCM (AFCM) approach is proposed. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the system and the interpretability of the decision mechanism. The proposed method is tested utilizing a CAD dataset from the Laboratory of Nuclear Medicine of the University of Patras. More specifically, two architectures of AFCMs are designed, and different parameter testing is performed. Furthermore, the proposed AFCMs, which are based on the new equations proposed recently, are compared with the traditional FCM approach. Results: The experiments highlight the effectiveness of the AFCM approach and the new equations over the traditional approach, which obtained an accuracy of 78.21%, achieving an increase of seven percent (+7%) on the classification task, and obtaining 85.47% accuracy. Conclusions: 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. Conclusions and future research related to recent pandemic of coronavirus are provided.

中文翻译：

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用于冠状动脉疾病自动和非侵入性诊断的状态空间高级模糊认知图方法

目的：在本研究中，对模糊认知图（FCM）最近出现的进展进行了调查和利用，以实现冠状动脉疾病（CAD）的自动和非侵入性诊断。方法：提出了一种计算机辅助诊断模型，用于使用状态空间高级 FCM (AFCM) 方法对 CAD 进行可接受的无创预测。此外，还引入了基于规则的机制，以进一步增加系统的知识和决策机制的可解释性。利用来自帕特雷大学核医学实验室的 CAD 数据集对所提出的方法进行了测试。更具体地说，设计了两种AFCM架构，并进行了不同的参数测试。此外，所提出的 AFCM 是基于最近提出的新方程，与传统的 FCM 方法进行比较。结果：实验突出了AFCM方法和新方程相对于传统方法的有效性，获得了78.21%的准确率，在分类任务上实现了百分之七（+7%）的提高，获得了85.47%的准确率。结论：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。提供了与近期冠状病毒大流行相关的结论和未来研究。在分类任务上实现了百分之七（+7%）的提升，并获得了 85.47% 的准确率。结论：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。提供了与近期冠状病毒大流行相关的结论和未来研究。在分类任务上实现了百分之七（+7%）的提升，并获得了 85.47% 的准确率。结论：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。提供了与近期冠状病毒大流行相关的结论和未来研究。