KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction
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 e...
| Authors | Rahul Goswami |
|---|---|
| Primary category | stat.ML |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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