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New variations of random survival forests and applications to age-related disease data

Pomsuwan, Tossapol, Freitas, Alex A. (2022) New variations of random survival forests and applications to age-related disease data. In: Proceedings of the 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). . pp. 1-10. IEEE ISBN 978-1-66546-845-9. (In press) (doi:10.1109/ICHI2022.2022.00013) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:95532)

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https://doi.org/10.1109/ICHI2022.2022.00013

Abstract

This work addresses a type of survival prediction (or survival analysis) problem, where the goal is to predict the time passed until an individual is diagnosed with a certain age-related disease. Survival prediction is more challenging than standard regression because the former involves censored data, i.e. individuals who have not been diagnosed with the disease yet. Random Survival Forests (RSFs) are a powerful type of Random Forest algorithm developed specifically for survival analysis. In this work we investigate new variations of RSFs, namely variations in the node-splitting criterion and the leaf-node-prediction criterion. Results of experiments on 10 real-world survival prediction problems show that, although the variations in node-splitting criteria did not lead to significant differences in predictive performance, RSFs with a new proposed leaf-node-prediction criterion had significantly better predictive performance than standard RSFs.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ICHI2022.2022.00013
Uncontrolled keywords: Random forest, Regression tree, Survival analysis, Censored data, Age-related diseases
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 24 Jun 2022 11:08 UTC
Last Modified: 27 Jun 2022 08:45 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/95532 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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