The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

COVID-19 大流行于 2020 年初爆发并被证明是致命的，在全球范围内造成了许多人员伤亡。立即、精确地筛查受影响的患者对于疾病控制至关重要。由于症状相似，COVID-19 经常与其他各种呼吸系统疾病混淆。截至目前，逆转录聚合酶链反应 (RT-PCR) 测试已用于诊断 COVID-19。然而，这种方法有时容易产生错误和假阴性结果。因此，找到一种可以验证 RT-PCR 检测结果的可靠诊断方法至关重要。事实证明，人工智能 (AI) 和机器学习 (ML) 在 COVID-19 诊断中的应用是有益的。因此，在本研究中的几个分类器的帮助下，临床标志物已被用于 COVID-19 诊断。此外，还利用五种不同的可解释人工智能技术来解释预测。在所有算法中，k-近邻算法获得了最好的性能，准确率、精确率、召回率和 f1 分数分别为 84%、85%、84% 和 84%。根据这项研究，嗜酸性粒细胞、淋巴细胞、红细胞和白细胞等临床标志物的组合对于区分 COVID-19 具有重要意义。该分类器可以与标准 RT-PCR 程序同步使用，使诊断更加可靠和高效。