Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model

摘要

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.