Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges. Data science and machine learning (ML), for example, can aid in the battle against this pandemic. Furthermore, various research published in this direction proves that ML techniques can identify illness and viral infections more precisely, allowing patients' diseases to be detected at an earlier stage. In this paper, we will present how ontologies can aid in predicting the presence of covid-19 based on symptoms. The integration of ontology and ML is achieved by implementing rules of the decision tree algorithm into ontology reasoner. In addition, we compared the outcomes with various ML classifications used to make predictions. The findings are assessed using performance measures generated from the confusion matrix, such as F-measure, accuracy, precision, and recall. The ontology surpassed all ML algorithms with high accuracy value of 97.4%, according to the results.
翻译:科罗纳病毒(covid 19)是传播到世界各地的最危险的病毒之一,由于感染冠状病毒的病例越来越多,因此有必要以所有可用的手段来应对这一流行病。检测covid-19是目前世界上最困难的挑战之一。例如,数据科学和机器学习(ML)可以帮助抗击这一流行病。此外,根据这个方向发表的各种研究证明,ML技术可以更准确地识别疾病和病毒感染,从而能够及早发现病人的疾病。在本文中,我们将介绍本科氏病如何有助于预测基于症状的 Covid-19 的存在。通过执行决定树算法规则,将肿瘤学和ML 整合到肿瘤解释器中。此外,我们将结果与用于预测的各种ML分类进行比较。通过使用混乱矩阵(如F-计量、准确度、精确度和回顾)产生的性能度评估结果。根据结果,肿瘤学超过所有ML算法的高度精确值为97.4%。