DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management Pub Date : 2022-01-01 Epub Date: 2022-07-04 DOI:10.1007/s41870-022-00973-2

Tarik Alafif, Alaa Etaiwi, Yousef Hawsawi, Abdulmajeed Alrefaei, Ayman Albassam, Hassan Althobaiti

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Abstract

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients' data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients' data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients' data.

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DISCOVID：从康复患者中发现 COVID-19 的感染模式：沙特阿拉伯的案例研究。

COVID-19 呼吸道综合征大流行已成为全球严重关切的问题。尽管如此，全球每天仍有大量人员受到感染。发现 COVID-19 的感染模式对于医疗工作者了解感染因素意义重大。目前的 COVID-19 研究工作尚未尝试发现感染模式。本文采用关联规则 Apriori (ARA) 算法，从 COVID-19 患者恢复数据中发现感染模式。本文介绍并分析了一个非临床 COVID-19 数据集。该数据集是在沙特阿拉伯人工收集的康复患者数据样本。我们的人工计算和实验结果表明，在男性、体重超过 70 公斤、身高超过 160 厘米和发烧模式中，具有较高置信度的关联规则很强。这些模式是从 COVID-19 恢复患者数据中发现的最强感染模式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

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