Survival Analysis Models for Mortality Risk Prediction during the Pandemic: A Comparative Study Using Cox Proportional Hazards, Random Survival Forests, and DeepSurv

As the COVID-19 pandemic continues to place immense pressure on healthcare systems globally, there is an urgent need for analytical tools that can identify key factors affecting patient outcomes and support clinical decision-making. This study analyzes critical factors influencing outcomes among COVID-19 patients by comparing the Cox proportional hazards (CPH), random survival forests (RSF), and DeepSurv (DS) survival analysis models, using data from RSUD Dr. Kanujoso Djatiwibowo, a large public referral hospital in Balikpapan, Indonesia. Results show that RSF achieves the best performance (C-index = 0.8462; AUC = 0.7186). The analysis further reveals that older male patients requiring mechanical ventilation and ICU care experience significantly reduced survival probabilities, impacting both length of hospital stay and mortality risk. Additionally, oxygen saturation consistently emerges as the most influential predictor of mortality risk across all three models, highlighting its strong association with other clinical variables. This study contributes by comparing various survival analysis models, incorporating not only clinical variables but also comprehensive historical health conditions such as comorbidities and physiological parameters, and offering insights for decision-makers to prioritize key risk factors and optimize patient management during the COVID-19 crisis.