Elshennawy Nada M, Ibrahim Dina M, Sarhan Amany M, Arafa Mohamed
Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt.
Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.
Diagnostics (Basel). 2022 Jul 30;12(8):1847. doi: 10.3390/diagnostics12081847.
The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒在全球范围内扩散，夺走了许多人的生命，给所有人带来了恐慌。由于新型冠状病毒肺炎（COVID-19）具有高度传染性且传播迅速，早期诊断至关重要。识别COVID-19患者的死亡风险因素对于降低感染者的这种风险至关重要。为了及时检查大型数据集，必须创建新的计算方法。已经开发了许多机器学习（ML）技术来预测COVID-19患者的死亡风险因素和严重程度。与预期相反，深度学习方法以及ML算法尚未广泛应用于预测COVID-19的死亡率和严重程度。此外，ML算法所达到的准确率低于预期值。在这项工作中，使用了三种监督深度学习预测模型来预测COVID-19患者的死亡风险和严重程度。第一个模型，我们称为CV-CNN，是使用卷积神经网络（CNN）构建的；它使用12020名患者的临床数据集进行训练，并基于10折交叉验证（CV）方法进行训练和验证。第二个预测模型，我们称为CV-LSTM + CNN，是通过将长短期记忆（LSTM）方法与CNN模型相结合而开发的。它也使用基于10折CV方法的临床数据集进行训练和验证。前两个预测模型使用原始CSV格式的临床数据集。最后一个模型，我们称为IMG-CNN，是一个CNN模型，使用临床数据集的转换图像进行交替训练，其中每个图像对应于原始临床数据集中的一行数据。实验结果表明，IMG-CNN预测模型的平均准确率为94.14%，精确率为100%，召回率为91.0%，特异性为100%，F1分数为95.3%，AUC为93.6%，损失为0.22，优于其他两个模型。
