Dasgupta Shirin, Das Shuvankar, Chakraborty Debarghya
Dr. B. C. Roy Multi Speciality Medical Research Centre, Indian Institute of Technology Kharagpur, West Bengal, India.
Department of Civil Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.
J Family Med Prim Care. 2024 Jul;13(7):2683-2691. doi: 10.4103/jfmpc.jfmpc_1862_23. Epub 2024 Jun 28.
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.
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.
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.
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.
2019年至2022年期间，2019冠状病毒病（COVID-19）成为全球大流行疾病。检测该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，目标是提出一种利用机器学习（ML）技术检测COVID-19感染的廉价且有效的方法，该方法包含五个基本参数，作为昂贵的RT-PCR的替代方法。
设计了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），用于预测COVID-19感染，作为利用五个基本参数（即年龄、白细胞总数、红细胞计数、血小板计数、C反应蛋白（CRP））的比RT-PCR更便宜、更简单的替代方法。对这些参数中的每一个进行了研究，并与COVID-19的诊断和病程建立了相关性。2022年4月至8月，在印度哈格普尔的一家医院对171名出现COVID-19可疑症状的患者进行了这些实验室参数评估。在总共171名患者中，分别有88名和83名被发现为COVID-19阴性和COVID-19阳性。
发现ANN和MARS预测类别的准确率分别为97.06%和91.18%。发现CRP是最显著的输入参数。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感染可能非常有用。
预计本研究将有助于医生仅基于五个非常基本的参数预测患者的COVID-19感染情况。
