# Survival analysis as a classification problem

arXiv: https://arxiv.org/abs/1909.11171 · HTML: https://mlsanity.com/paper/1909.11171/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Tabular Foundation Models Can Do Survival Analysis](https://mlsanity.com/paper/2601.22259/) (2026)
- [Inverse-Weighted Survival Games](https://mlsanity.com/paper/2111.08175/) (2021)
- [Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures](https://mlsanity.com/paper/2112.04828/) (2021)
- [Survival Analysis as Imprecise Classification with Trainable Kernels](https://mlsanity.com/paper/2506.10140/) (2025)
- [Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks](https://mlsanity.com/paper/2204.11588/) (2022)

## Abstract (arXiv)

In this paper, we explore a method for treating survival analysis as a classification problem. The method uses a "stacking" idea that collects the features and outcomes of the survival data in a large data frame, and then treats it as a classification problem. In this framework, various statistical learning algorithms (including logistic regression, random forests, gradient boosting machines and neural networks) can be applied to estimate the parameters and make predictions. For stacking with logistic regression, we show that this approach is approximately equivalent to the Cox proportional hazards model with both theoretical analysis and simulation studies. For stacking with other machine learning algorithms, we show through simulation studies that our method can outperform Cox proportional hazards model in terms of estimated survival curves. This idea is not new, but we believe that it should be better known by statistiicians and other data scientists.