PDF(Pubmed)
Abstract：
UNASSIGNED: Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR.
UNASSIGNED: Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively.
UNASSIGNED: The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily.
UNASSIGNED: It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.
UNASSIGNED: Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively.
UNASSIGNED: The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily.
UNASSIGNED: It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.
摘要：
■2019年冠状病毒病（COVID-19）在2019年至2022年期间成为全球大流行。检测这种疾病的金标准方法是逆转录聚合酶链反应（RT-PCR）。然而,RT-PCR有许多缺点。因此，目的是通过使用机器学习（ML）技术提出一种廉价有效的检测COVID-19感染的方法，其中包含五个基本参数，可替代昂贵的RT-PCR。
■两种基于机器学习的预测模型，即,人工神经网络(ANN)和多元自适应回归样条(MARS)被设计用于预测COVID-19感染，作为利用五个基本参数的RT-PCR的更便宜、更简单的替代方法[，年龄,白细胞总数,红细胞计数,血小板计数,C反应蛋白（CRP）]。研究了这些参数中的每一个，与COVID-19的诊断和进展相关。在Kharagpur的一家医院对171名出现可疑COVID-19症状的患者进行了这些实验室参数评估，印度,2022年4月至8月。在总共171名患者中，88和83被发现是COVID-19阴性和COVID-19阳性，分别。
■对于ANN和MARS，预测类的准确度分别为97.06%和91.18%，分别。CRP被发现是最重要的输入参数。最后,为每个ML模型提供了两个预测数学方程，这对于轻松检测COVID-19感染非常有用。
■预计本研究将有助于医生仅根据五个非常基本的参数预测患者的COVID-19感染。
■两种基于机器学习的预测模型，即,人工神经网络(ANN)和多元自适应回归样条(MARS)被设计用于预测COVID-19感染，作为利用五个基本参数的RT-PCR的更便宜、更简单的替代方法[，年龄,白细胞总数,红细胞计数,血小板计数,C反应蛋白（CRP）]。研究了这些参数中的每一个，与COVID-19的诊断和进展相关。在Kharagpur的一家医院对171名出现可疑COVID-19症状的患者进行了这些实验室参数评估，印度,2022年4月至8月。在总共171名患者中，88和83被发现是COVID-19阴性和COVID-19阳性，分别。
■对于ANN和MARS，预测类的准确度分别为97.06%和91.18%，分别。CRP被发现是最重要的输入参数。最后,为每个ML模型提供了两个预测数学方程，这对于轻松检测COVID-19感染非常有用。
■预计本研究将有助于医生仅根据五个非常基本的参数预测患者的COVID-19感染。
