Deep Learning Frailty Model for Heart Failure Survival Prediction

Norouzi, Solmaz; Khormaei, Hossein; Hajizadeh, Ebrahim; Naderi, Nasim

doi:10.48308/jicse.2025.241654.1085

Deep Learning Frailty Model for Heart Failure Survival Prediction

Document Type : Original Article

Authors

Solmaz norouzi ¹

Hossein Khormaei ²

Ebrahim Hajizadeh ³

nasim naderi ⁴

¹ University of Qazvin, Zanjan University of Medical Sciences

² Department of Electrical Engineering, National University of Skills (NUS), Tehran, Iran.

³ Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.

⁴ Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.

https://doi.org/10.48308/jicse.2025.241654.1085

Abstract

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Abstract—The study employed Deep Learning Frailty (DLF), a compelling neural modeling framework for predicting heart failure patient survival. The DLF embeds a notion of multiplicative frailty from classical survival analysis that deals with unobserved heterogeneity while exploiting the neural structure's strong capabilities in approximating any non-linear covariate relationship. The results showed that Incorporating frailty leads to significant improvements, and the DLF model performs better on average.

Keywords

Keywords—Deep Learning

prediction

survival analysis

heart failure

Subjects