Adapting random forests to cope with heavily censored datasets in survival analysis
Tossapol Pomsuwan, Alex Alves Freitas · Kent Academic Repository (University of Kent) · 2020
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.