Table 8.

Techniques, attributes, and characteristics of survival analysis-COVID-19 applications

Authors	The basic objective	Pros	Limitations in study	Security method?	Simulation environments	Dataset and Size of Dataset	Using TL?	Mechanism;	Application?
Schwab, Mehrjou [108]	Utilizing the WOA approach to improve GAN's parameters	-High interpretability;-High scalability;	-High complexity;	No	Python	66,430 COVID-19 patients (Large dataset)	No	Time-varying neural Cox mode	Survival analysis
Uemura, Näppi [109]	Developing a conditional GAN that allows for a direct estimate of the survival time distribution	-High C-index;-High REA	-Low flexibility	No	Not mentioned	Database of 214 COVID-19 patients;(Small size dataset)	No	GAN	Survival analysis
Sinha and Rathi [110]	We present a prediction analysis of quarantined COVID-19 instances using several artificial DL models and hyperparameter optimization	-High accuracy;-High scalability	-High energy consumption	No	Python	The dataset contained 5165 cases that were verified on 1533 quarantined patients. (Medium size dataset)	No	Autoencoder	Survival prediction
Näppi, Uemura [111]	Proposing U-Net for semantic lung segmentation of axial CT images into five unique lung tissue patterns	-High binary classification ability	-Low scalability;-Low robustness	No	PyTorch	Dataset included 383 patients;(Small dataset)	No	U-Net (CNN)	Survival prediction model