Abstract
OBJECTIVES: Heart failure (HF) is a complicated disease with several competing risks of interest, such as HF death and other causes. This study compares a Deep Neural Network Competing Risks (DNNCR) and Random Survival Forest (RSF) model to evaluate the predictive performance of time-to-event outcomes in HF patients with competing risks. METHODS: This study represents the retrospective analysis of 435 HF patients admitted to RCMRC, Tehran, Iran, between March and August 2018. After a five-year follow-up in 2023, predictions were analyzed based on Cause of death. This study employed RSF and DNN methods to account for competing risks in survival analysis. Then, model fitness was applied using C-index and IBS. RESULTS: The C-index of the results shows that DNNCR is superior to RSF in predicting survival outcomes for HF and other causes of death. Precisely, the C-index was 0.65 (0.04) for HF and 0.63 (0.02) for other causes of death in the DNNCR model, while in RSF, the C-index was 0.65 (0.04) for HF and 0.61 (0.03) for Other Causes. Additionally, calibration results showed via the IBS the finest performance of the DNNCR model at 0.16 for HF, followed by other causes with an IBS of 0.18. CONCLUSIONS: The study shows that the DNNCR model outperforms RSF in predicting survival outcomes for HF patients, particularly in the presence of competing risks. The improved accuracy enables physicians to identify high-risk individuals and tailor treatment plans accordingly. Future research could utilize more diverse datasets to enhance DNNCR performance and integrate these models into clinical tools.