Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis

Researchers propose using genetic programming to evolve interpretable feature sets and tree structures for survival analysis models, demonstrating improved predictive performance while maintaining shallow, explainable decision trees. The approach addresses the fundamental trade-off between accuracy and interpretability in medical survival prediction by optimizing both feature construction and tree logic simultaneously.

This research addresses a critical challenge in machine learning for healthcare: building models that are both accurate and interpretable. Survival analysis—predicting time-to-event outcomes with censored data—is essential for clinical applications, yet traditional approaches struggle with competing demands. Greedy tree-building algorithms often trap models in local optima, while deep trees sacrifice explainability for performance gains.

The researchers' genetic programming approach tackles this by evolving feature combinations and tree structures together, rather than sequentially. This multi-objective optimization enables shallow trees to achieve competitive accuracy through higher-order feature interactions, maintaining clinical interpretability. Their methodology tests two strategies: evolutionary feature construction paired with standard tree induction, and full joint evolution of both features and tree logic.

For healthcare AI development, this represents a meaningful advance toward deployment-ready models. Medical practitioners need transparent decision-making processes for regulatory compliance and clinical trust. Shallow, interpretable trees with engineered features provide explainability advantages over black-box alternatives, while evolutionary approaches mitigate the greedy algorithm limitations that sacrifice global optimization.

The dual-dataset validation across varying tree depths demonstrates generalizability rather than isolated improvements. As healthcare systems increasingly adopt AI for treatment planning and risk stratification, methods balancing accuracy with interpretability become competitive advantages. Future work likely extends this approach to multi-outcome predictions and real-time clinical adaptation, positioning evolutionary computation as a practical bridge between statistical transparency and modern predictive power in medical applications.

• →Genetic programming optimizes survival tree features and structures simultaneously, improving predictive accuracy while maintaining interpretability.
• →Shallow survival trees with evolved feature combinations outperform traditional greedy approaches across multiple datasets.
• →Multi-objective evolutionary approaches enable healthcare models to satisfy both regulatory transparency and clinical performance requirements.
• →Higher-order feature engineering compensates for shallow tree depth, preserving model explainability crucial for medical applications.
• →Full joint evolution of tree structure and split logic demonstrates greater potential for generating multiple competitive shallow survival models.