Leveraging neural networks for robust survival estimation in cox proportional hazards model with censored data

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Survival analysis, traditionally dominated by models like the Cox Proportional Hazards, faces significant challenges in handling high-dimensional and complex datasets common in modern medical and biological research. This paper introduces an innovative approach to address these challenges by leveraging Variational Autoencoders (VAE) for feature extraction before survival analysis. We propose a framework where VAE compress high-dimensional input data into a lower-dimensional, yet informative latent space. Also, we identify key factors associated with mortality. We use publicly available Covid-19 data and analyze the survival characteristics of a large sample of 566,602 patients. Various machine learning techniques compared to improve prediction accuracy. The performance of the models has been evaluated using metrics like the concordance index and accuracy. The proposed algorithm performs the best with an accuracy of 0.94. Also, it shows good concordance, with training and test concordance index values of 0.91559 and 0.91299, respectively, while other algorithms range from 70% to 91% accuracy. The findings suggest that most features have a significant impact on reducing mortality, except for COPD, asthma, inmsupr, cardiovascular, obesity, renal chronic and tobacco. This model can help identify patients at high risk of death, so they can be prioritized for critical care.