KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction

Abstract:Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Among the various models available, the Survival Forest has demonstrated significant effectiveness in numerous scenarios. In this work, we propose an ensemble method that leverages the strengths of the Survival Forest as the master model, complemented by several base models. This ensemble incorporates the Beran estimator, a type of kernel estimator, to enhance predictions of patient-specific survival curves. We evaluated the performance of our proposed model using four distinct healthcare datasets. The results highlight the superiority of our ensemble method over baseline models in both calibration and ranking across most datasets. The findings suggest that our approach offers a more accurate and reliable estimation of patient-specific survival functions, providing a valuable tool for clinical decision-making.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.06434 [stat.ML]

(or arXiv:2610.06434v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.06434 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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