Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that hospital places are not available for all patients. Therefore, most hospitals accept patients whose recovery rate is high. Machine learning techniques and artificial intelligence have been deployed for computing infection risks, performing survival analysis and classification. Survival analysis (time-to-event analysis) is widely used in many areas such as engineering and medicine. This paper presents two systems, Cox_COVID_19 and Deep_ Cox_COVID_19 that are based on Cox regression to study the survival analysis for COVID-19 and help hospitals to choose patients with better chances of survival and predict the most important symptoms (features) affecting survival probability. Cox_COVID_19 is based on Cox regression and Deep_Cox_COVID_19 is a combination of autoencoder deep neural network and Cox regression to enhance prediction accuracy. A clinical dataset for COVID-19 patients is used. This dataset consists of 1085 patients. The results show that applying an autoencoder on the data to reconstruct features, before applying Cox regression algorithm, would improve the results by increasing concordance, accuracy and precision. For Deep_ Cox_COVID_19 system, it has a concordance of 0.983 for training and 0.999 for testing, but for Cox_COVID_19 system, it has a concordance of 0.923 for training and 0.896 for testing. The most important features affecting mortality are, age, muscle pain, pneumonia and throat pain. Both Cox_COVID_19 and Deep_ Cox_COVID_19 prediction systems can predict the survival probability and present significant symptoms (features) that differentiate severe cases and death cases. But the accuracy of Deep_Cox_Covid_19 outperforms that of Cox_Covid_19. Both systems can provide definite information for doctors about detection and intervention to be taken, which can reduce mortality.

冠状病毒 (COVID-19) 是导致全世界生命之轮停止的最严重问题之一。它广泛传播，以至于并非所有患者都可以使用医院。因此，大多数医院接受康复率高的患者。机器学习技术和人工智能已被用于计算感染风险、执行生存分析和分类。生存分析（时间到事件分析）广泛用于工程和医学等许多领域。本文提出了两个基于 Cox 回归的系统 Cox_COVID_19 和 Deep_Cox_COVID_19 来研究 COVID-19 的生存分析，并帮助医院选择具有更好生存机会的患者并预测影响生存概率的最重要的症状（特征）。Cox_COVID_19 基于 Cox 回归，Deep_Cox_COVID_19 是自动编码器深度神经网络和 Cox 回归的组合，以提高预测准确性。使用了 COVID-19 患者的临床数据集。该数据集由 1085 名患者组成。结果表明，在应用 Cox 回归算法之前，对数据应用自动编码器以重建特征，将通过提高一致性、准确性和精度来改进结果。对于 Deep_Cox_COVID_19 系统，它的训练一致性为 0.983，测试一致性为 0.999，但对于 Cox_COVID_19 系统，训练一致性为 0.923，测试一致性为 0.896。影响死亡率的最重要特征是年龄、肌肉疼痛、肺炎和喉咙痛。Cox_COVID_19 和 Deep_Cox_COVID_19 预测系统都可以预测生存概率并呈现区分严重病例和死亡病例的显着症状（特征）。但 Deep_Cox_Covid_19 的准确性优于 Cox_Covid_19。这两个系统都可以为医生提供有关检测和干预的明确信息，从而降低死亡率。