Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

中文翻译：

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基于模糊认知图的胃癌危险因素医学决策支持系统

胃癌（GC）是世界上最常见的癌症之一，是一种多因素疾病，该疾病有许多危险因素。评估GC风险对于选择合适的医疗策略至关重要。关于GC风险评估系统开发的研究很少。这项研究旨在提供基于使用模糊认知图（FCM）的软计算的医疗决策支持系统，该系统将帮助医疗保健专业人员根据疾病的风险水平来决定合适的个人医疗策略。FCM被认为是用于复杂系统建模的最强大的人工智能技术之一。在该系统中，使用了基于非线性Hebbian学习（NHL）算法的FCM。这项研究中使用的数据是从560名患者的病历中收集的，这些患者来自大不里士市的Imam Reza医院。根据三位专家的意见，选择了27种有效的胃癌特征。所提方法的预测精度为95.83％。结果表明，所提出的方法比其他决策算法（如决策树，朴素贝叶斯和人工神经网络）更准确。从医疗保健专业人员的角度来看，所提出的医疗决策支持系统比以前的模型更简单，全面，并且更有效地评估了GC的风险，并且可以帮助他们预测临床环境中GC的危险因素。所提方法的预测精度为95.83％。结果表明，所提出的方法比其他决策算法（如决策树，朴素贝叶斯和人工神经网络）更准确。从医疗保健专业人员的角度来看，所提出的医疗决策支持系统比以前的模型更简单，全面，并且更有效地评估了GC的风险，并且可以帮助他们预测临床环境中GC的危险因素。所提方法的预测精度为95.83％。结果表明，所提出的方法比其他决策算法（如决策树，朴素贝叶斯和人工神经网络）更准确。从医疗保健专业人员的角度来看，所提出的医疗决策支持系统比以前的模型更简单，全面，并且更有效地评估了GC的风险，并且可以帮助他们预测临床环境中GC的危险因素。