The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 patient's records, identified the most common signs and symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively.

翻译：COVID-19大流行对全球造成了毁灭性影响，夺走了数百万人的生命，并引发了严重的社会经济 disruption。为了优化决策并分配有限资源，识别COVID-19症状并判断每个病例的严重程度至关重要。机器学习算法为医学领域提供了强大工具，尤其在挖掘临床数据集中的有用信息及指导科学决策方面具有显著作用。关联规则挖掘是一种从数据中提取隐藏模式的机器学习技术。本文提出了一种基于Apriori算法的关联规则挖掘方法，用于发现COVID-19患者的症状模式。该研究利用2875名患者的记录，识别出最常见的体征和症状包括呼吸暂停（72%）、咳嗽（64%）、发热（59%）、乏力（18%）、肌痛（14.5%）和咽喉痛（12%）。所提出的方法为临床医生提供了对疾病的宝贵洞察，有助于有效管理和治疗该疾病。