Summary
Objectives: Rule induction is one of the major methods of machine learning. Rule-based models
can be easily read and interpreted by humans, that makes them particularly useful
in survival studies as they can help clinicians to better understand analysed data
and make informed decisions about patient treatment. Although of such usefulness,
there is still a little research on rule learning in survival analysis. In this paper
we take a step towards rule-based analysis of survival data.
Methods: We investigate so-called covering or separate-and-conquer method of rule induction
in combination with a weighting scheme for handling censored observations. We also
focus on rule quality measures being one of the key elements differentiating particular
implementations of separate-and-conquer rule induction algorithms. We examine 15 rule
quality measures guiding rule induction process and reflecting a wide range of different
rule learning heuristics.
Results: The algorithm is extensively tested on a collection of 20 real survival datasets
and compared with the state-of-the-art survival trees and random survival forests
algorithms. Most of the rule quality measures outperform Kaplan-Meier estimate and
perform at least equally well as tree-based algorithms.
Conclusions: Separate-and-conquer rule induction in combination with weighting scheme is an effective
technique for building rule-based models of survival data which, according to predictive
accuracy, are competitive with tree-based representations.
Keywords
Survival prediction - rule induction - rule quality measures
