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Hybrid Survival Modeling for Multi-Stage Cardiovascular Risk using ELU-Activated Deep Surv and MTLR Ensembles with Temporal Feature Fusion
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 68 |
| Year of Publication: 2025 |
| Authors: Uthama Kumar A., Rashmi Makal |
10.5120/ijca2025926009
|
Uthama Kumar A., Rashmi Makal . Hybrid Survival Modeling for Multi-Stage Cardiovascular Risk using ELU-Activated Deep Surv and MTLR Ensembles with Temporal Feature Fusion. International Journal of Computer Applications. 187, 68 ( Dec 2025), 8-12. DOI=10.5120/ijca2025926009
Abstract
Cardiovascular disease (CVD) is a leading cause of global mortality. The accurate prediction of a patient’s time-to-event outcomes, such as heart failure or mortality, is a critical challenge in clinical practice. Traditional survival models, including the Cox Proportional Hazards (CoxPH) model and Multi-Task Logistic Regression (MTLR), often rely on simplifying assumptions that limit their ability to capture complex, non-linear relationships and temporal dynamics inherent in patient health data. This research introduces a novel hybrid survival model that integrates an ELU-activated Deep Surv network with an MTLR ensemble, enhanced by a temporal feature fusion layer. The model is designed to leverage the complementary strengths of both approaches: Deep Surv’s capacity for learning deep, nonlinear representations and MTLR’s ability to estimate time-specific survival probabilities. By fusing static patient attributes (e.g., age, gender) with dynamic temporal features (e.g., blood pressure readings over time), the model provides a more comprehensive view of a patient’s health trajectory. We train the model using a survival-specific loss function and evaluate its performance using metrics such as the C-index, Integrated Brier Score (IBS), and time-dependent AUC. When applied to the Framingham Heart Study dataset, the hybrid model consistently outperformed traditional methods, yielding higher predictive accuracy and more clinically interpretable risk stratification. This approach demonstrates a promising step towards developing personalized, time-aware predictive analytics for cardiovascular care.
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