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An accelerated failure time boosting algorithm based on neural additive models
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AI
DOI：10.1080/02664763.2026.2661267.png)
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Abstract
En 中文
This paper proposes a new interpretable ensemble learning algorithm for survival data, an accelerated failure time boosting algorithm based on neural additive models (AFTBoost-NAMs), which is an extension of neural additive models and the gradient boosting algorithm to study survival data with right-censored data. The first step of the AFTBoost-NAMs algorithm is to replace the linear combination of covariates in the accelerated failure time model based on rank loss with the output function of the neural additive models, and the second step is the optimization of the model using the framework of the gradient boosting algorithm. Furthermore, to evaluate the performance of the new proposed AFTBoost-NAMs algorithm, we compare AFTBoost-NAMs with four other famous survival regression models on 4 simulated and 6 real datasets, using two evaluation measures, the concordance index and extended mean squared error. We use the prostate cancer dataset as an example to analyze the interpretability of the model. According to the comparison results in the testing dataset and global interpretation in the training dataset, the new proposed AFTBoost-NAMs performs well in terms of prediction accuracy and is effective in terms of interpretability.
Keywords:
Accelerated failure time
neural additive models
boosting algorithm
interpretability
Journal
J
IF:
1.1
Papers:
131
Citations:
4.2K
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