The Reverse Transcription Polymerase Chain Reaction (RTPCR)} test is the silver bullet diagnostic test to discern COVID infection. Rapid antigen detection is a screening test to identify COVID positive patients in little as 15 minutes, but has a lower sensitivity than the PCR tests. Besides having multiple standardized test kits, many people are getting infected and either recovering or dying even before the test due to the shortage and cost of kits, lack of indispensable specialists and labs, time-consuming result compared to bulk population especially in developing and underdeveloped countries. Intrigued by the parametric deviations in immunological and hematological profile of a COVID patient, this research work leveraged the concept of COVID-19 detection by proposing a risk-free and highly accurate Stacked Ensemble Machine Learning model to identify a COVID patient from communally available-widespread-cheap routine blood tests which gives a promising accuracy, precision, recall and F1-score of 100%. Analysis from R-curve also shows the preciseness of the risk-free model to be implemented. The proposed method has the potential for large scale ubiquitous low-cost screening application. This can add an extra layer of protection in keeping the number of infected cases to a minimum and control the pandemic by identifying asymptomatic or pre-symptomatic people early.

翻译：逆转录聚合酶链反应（RTPCR）检测是诊断COVID感染的金标准检测方法。快速抗原检测可在15分钟内识别COVID阳性患者，但其灵敏度低于PCR检测。尽管有多种标准化检测试剂盒，但由于试剂盒短缺和成本高昂、缺乏专业实验室及技术人员、与大规模人群相比检测耗时过长等问题，尤其在发展中国家和不发达国家，许多人在检测前就已感染并痊愈或死亡。受COVID患者免疫学和血液学参数偏差的启发，本研究提出了一种零风险且高精度的堆叠集成机器学习模型，通过广泛可用的低成本常规血液检测来识别COVID患者。该模型在准确率、精确率、召回率和F1分数上均达到100%。R曲线分析也验证了该零风险模型的精确性。所提方法具有大规模、普适性、低成本筛查应用的潜力。通过早期识别无症状或症状前感染者，该方法可为最大限度地减少感染病例数量、控制疫情提供额外防护层。