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Censoring weighted separate-and-conquer rule induction from survival data.

Ł Wróbel1, M Sikora

  • 1Łukasz Wróbel, Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland,

Methods of Information in Medicine
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Summary

This study introduces a rule induction method for survival analysis, demonstrating its effectiveness in creating interpretable models. The approach, using separate-and-conquer with a weighting scheme, shows competitive predictive accuracy against established algorithms.

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Survival predictionrule inductionrule quality measures

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Area of Science:

  • Machine learning
  • Biostatistics
  • Data mining

Background:

  • Rule-based models offer human-interpretable insights crucial for clinical decision-making in survival studies.
  • Limited research exists on applying rule induction techniques specifically to survival analysis.
  • This work addresses the need for advanced rule learning methods in survival data analysis.

Purpose of the Study:

  • To develop and evaluate a rule induction method for survival data analysis.
  • To investigate the separate-and-conquer approach combined with a weighting scheme for censored survival data.
  • To assess the performance of various rule quality measures in guiding rule induction.

Main Methods:

  • Utilized the separate-and-conquer rule induction algorithm.
  • Incorporated a weighting scheme to handle censored survival observations.
  • Examined 15 distinct rule quality measures to guide the induction process.

Main Results:

  • The proposed algorithm was tested on 20 real-world survival datasets.
  • Performance was compared against state-of-the-art survival trees and random survival forests.
  • Most rule quality measures outperformed the Kaplan-Meier estimate and matched tree-based algorithms in accuracy.

Conclusions:

  • Separate-and-conquer rule induction with a weighting scheme is effective for survival data.
  • The resulting rule-based models demonstrate competitive predictive accuracy.
  • This technique offers a viable alternative to tree-based models for survival analysis.