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Adapting random forests to cope with heavily censored datasets in survival analysis

Pomsuwan, Tossapol, Freitas, Alex A. (2020) Adapting random forests to cope with heavily censored datasets in survival analysis. In: Proceedings of the 2020 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020). . pp. 697-702. dblp ISBN 978-2-87587-074-2. (KAR id:83340)

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We address a survival analysis task where the goal is to predict the time passed until a subject is diagnosed with an age-related

disease. The main challenge is that subjects’ data are very often censored, i.e., their time to diagnosis is only partly known. We propose a new Random Forest variant to cope with censored data, and evaluate it in experiments predicting the time to diagnosis of 8 age-related diseases, for data from the English Longitudinal Study of Ageing (ELSA) database. In these experiments, the proposed Random Forest variant, in general, outperformed a well-known Random Forest variant for censored data.

Item Type: Conference or workshop item (Proceeding)
Uncontrolled keywords: machine learning, data mining, random forests, survival analysis
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 08 Oct 2020 21:16 UTC
Last Modified: 16 Feb 2021 14:15 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
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