Methodology for Comparing Machine Learning Algorithms for Survival Analysis

• URL: http://arxiv.org/abs/2510.24473v1
• Date: Tue, 28 Oct 2025 14:42:28 GMT
• Title: Methodology for Comparing Machine Learning Algorithms for Survival Analysis
• Authors: Lucas Buk Cardoso, Simone Aldrey Angelo, Yasmin Pacheco Gil Bonilha, Fernando Maia, Adeylson Guimarães Ribeiro, Maria Paula Curado, Gisele Aparecida Fernandes, Vanderlei Cunha Parro, Flávio Almeida de Magalhães Cipparrone, Alexandre Dias Porto Chiavegatto Filho, Tatiana Natasha Toporcov,
• Abstract summary: Six machine learning models for survival analysis were evaluated.<n>XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532, followed by GBSA and RSF)
• Score: 55.65997641180011
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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