Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

中文翻译：

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用于确定缺血性中风风险的基于模糊认知图的方法。

中风是世界上第三大死亡原因。脑卒中的诊断是一个考虑可控和不可控因素的非常复杂的问题。这些因素包括年龄、性别、血压、糖尿病、肥胖、心脏病、吸烟等，对中风的诊断有相当大的影响。因此，设计一个能够立即有效治疗的智能系统至关重要。在这项研究中，提出了一种称为模糊认知映射的软计算方法来诊断缺血性中风的风险。非线性Hebbian学习方法用于模糊认知图训练。在所提出的方法中，每个人的风险率是根据神经科医生的意见确定的。使用 10 倍交叉验证对 110 个真实案例测试了所提出模型的准确性，并将结果与​​支持向量机和K-最近邻的结果进行了比较。所提出的系统表现出卓越的性能，总准确度为 (93.6 ± 4.5)%。本研究中使用的数据可通过向第一作者发送电子邮件获得，用于学术和非商业目的。