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 & 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 & 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 & 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:反转定式聚合酶链反应(RTPCR)测试是识别COVID感染情况的银弹诊断测试。快速抗原检测是一种筛查测试,目的是在短短15分钟内识别COVID阳性病人,但比PCR测试敏感度较低。除了多套标准化测试包外,许多人正在感染,甚至恢复或甚至在测试之前死亡,原因是包具短缺和费用高,缺少不可或缺的专家和实验室,与大量人口相比,特别是在发展中国家和发达国家,时间耗费很大。受COVID病人免疫学和血貌特征偏差的刺激,这项研究工作利用COVID-19检测概念,提出无风险和高度准确的固定组合机体学习模式,以便从社区可获取的全发式切片常规血液测试中找出COVID病人,这提供了有希望的准确性、准确性、回顾和F1-记录率的100%。RCurvey分析还表明,要实施的无风险模型的精确性偏差,该模型利用COVID-19检测概念的概念,提出了一种无风险的最小值方法,通过高层次的常规检测来确定受感染人群的早期控制案例。这一方法,可以用来确定受感染者的早期检测。