Shahid Beheshti University Journal of Innovations in Computer Science and Engineering (JICSE) 2981-2135 3 1 2025 06 01 Deep Learning Frailty Model for Heart Failure Survival Prediction 48 52 106266 10.48308/jicse.2025.241654.1085 EN Solmaz Norouzi University of Qazvin, Zanjan University of Medical Sciences Hossein Khormaei Department of Electrical Engineering, National University of Skills (NUS), Tehran, Iran. Ebrahim Hajizadeh Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. Nasim Naderi Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. Journal Article 2025 09 22 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. <br /><br />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. <br /><br />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. https://jicse.sbu.ac.ir/article_106266_14250901d59083600d859963c0d5d587.pdf