Survival Analysis as Imprecise Classification with Trainable Kernels

Andrei Konstantinov, Lev Utkin, Vlada Efremenko, Vladimir Muliukha (), Alexey Lukashin and Natalya Verbova
Additional contact information
Andrei Konstantinov: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Lev Utkin: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Vlada Efremenko: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Vladimir Muliukha: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Alexey Lukashin: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Natalya Verbova: Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia

Mathematics, 2025, vol. 13, issue 18, 1-31

Abstract: Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While traditional methods like the Beran estimator offer nonparametric solutions, they often struggle with the complex data structures and heavy censoring. This paper introduces three novel survival models, iSurvM ( i mprecise Surv ival model based on M ean likelihood functions), iSurvQ ( i mprecise Surv ival model based on Q uantiles of likelihood functions), and iSurvJ ( i mprecise Surv ival model based on J oint learning), that combine imprecise probability theory with attention mechanisms to handle censored data without parametric assumptions. The first idea behind the models is to represent censored observations by interval-valued probability distributions for each instance over time intervals between event moments. The second idea is to employ the kernel-based Nadaraya–Watson regression with trainable attention weights for computing the imprecise probability distribution over time intervals for the entire dataset. The third idea is to consider three decision strategies for training, which correspond to the proposed three models. Experiments on synthetic and real datasets demonstrate that the proposed models, especially iSurvJ, consistently outperform the Beran estimator from accuracy and computational complexity points of view. Codes implementing the proposed models are publicly available.

Keywords: survival analysis; censored data; classification; attention mechanism; imprecise probabilities; Beran estimator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/18/3040/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/18/3040/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:18:p:3040-:d:1754314

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().