Since recording the first case in Wuhan in November 2020, COVID-19 is still spreading widely and rapidly affecting the health of millions all over the globe. For fighting against this pandemic, numerous strategies have been made, where the early isolation is considered among the most effective ones. Proposing useful methods to screen and diagnose the patient’s situation for the purpose of specifying the adequate clinical management represents a significant challenge in diminishing the rates of mortality. Inspired from this current global health situation, we introduce a new autonomous process of decision-making that consists of two modules. The first module is the data analysis based on Bayesian network that is employed to indicate the coronavirus symptoms severity and then classify COVID-19 cases as severe, moderate or mild. The second module represents the decision-making based on association rules method that generates autonomously the adequate decision. To construct the model of Bayesian network, we used an effective method-oriented data for the sake of learning its structure. As a result, the algorithm accuracy in making the correct decision is 30% and in making the adequate decision is 70%. These experimental results demonstrate the importance of the suggested methods for decision-making.

中文翻译：

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基于关联规则和贝叶斯方法的 COVID-19 战斗新决策过程

自 2020 年 11 月在武汉记录首例病例以来，COVID-19 仍在广泛迅速地传播，影响着全球数百万人的健康。为了对抗这种流行病，已经制定了许多策略，其中早期隔离被认为是最有效的策略之一。提出有用的方法来筛查和诊断患者的情况以指定适当的临床管理是降低死亡率的重大挑战。受当前全球健康状况的启发，我们引入了一种新的自主决策过程，该过程由两个模块组成。第一个模块是基于贝叶斯网络的数据分析，用于指示冠状病毒症状的严重程度，然后将 COVID-19 病例分为严重、中度或轻度。第二个模块表示基于关联规则的决策方法，该方法自主地生成适当的决策。为了构建贝叶斯网络模型，我们使用了一种有效的面向方法的数据来学习其结构。结果，做出正确决策的算法准确率为 30%，做出适当决策的准确率为 70%。这些实验结果证明了建议的决策方法的重要性。