Methodology for Comparing Machine Learning Algorithms for Survival Analysis

TL;DR

This paper compares six machine learning models for survival analysis using a large colorectal cancer dataset, evaluating their predictive performance and interpretability to identify the most effective approach.

Contribution

It provides a comprehensive methodological comparison of MLSA models with hyperparameter tuning and performance metrics on real-world data.

Findings

XGB-AFT achieved the highest C-Index of 0.7618

GBSA and RSF also showed strong performance

Models can be interpreted using SHAP and permutation importance

Abstract

This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of S\~ao Paulo, we evaluated Random Survival Forest (RSF), Gradient Boosting for Survival Analysis (GBSA), Survival SVM (SSVM), XGBoost-Cox (XGB-Cox), XGBoost-AFT (XGB-AFT), and LightGBM (LGBM), capable of predicting survival considering censored data. Hyperparameter optimization was performed with different samplers, and model performance was assessed using the Concordance Index (C-Index), C-Index IPCW, time-dependent AUC, and Integrated Brier Score (IBS). Survival curves produced by the models were compared with predictions from classification algorithms, and predictor interpretation was conducted using SHAP and permutation importance. XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532), followed by GBSA and RSF. The results highlight the potential and applicability of MLSA to improve survival prediction and support decision making.