Global Induction of Oblique Survival Trees

Survival analysis focuses on the prediction of failure time and serves as an important prognostic tool, not solely confined to medicine but also across diverse fields. Machine learning methods, especially decision trees, are increasingly replacing traditional statistical methods which are based on assumptions that are often difficult to meet. The paper presents a new global method for inducing survival trees containing Kaplan–Mayer estimators in leaves. Using a specialized evolutionary algorithm, the method searches for oblique trees in which multivariate tests in internal nodes divide the feature space using hyperplanes. Specific variants of mutation and crossover operators have been developed, making evolution effective and efficient. The fitness function is based on the integrated Brier score and prevents overfitting taking into account the size of the tree. A preliminary experimental verification and comparison with classical univariate trees was carried out on real medical datasets. The evaluation results are promising.