Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Review of Random Survival Forest Method Publisher



Rezaei M1 ; Tapak L2 ; Alimohammadian M3, 4, 5 ; Sadjadi A4, 5 ; Yaseri M1
Authors

Source: Journal of Biostatistics and Epidemiology Published:2020


Abstract

Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially medical sciences, due to their high accuracy and non-parametric nature and applicability in high-dimensional data sets. This paper aims to provide a methodological review and how to use random survival forests in the analysis of right-censored survival data. Method: We present a review article based on the latest research in the PubMed database on random survival forest model methodology. Results: This article begins with an introduction to tree-based methods, ensemble algorithms, and random forest (RF) method, followed by random survival forest framework, bootstrapped data and out-of-bag (OOB) ensemble estimators, review of performance evaluation indicators, how to select important variables, and other advanced topics of random survival forests for time-to-event data. Conclusion: When analyzing right-censored survival data with high-dimensional data, while the relationships between variables are complex and their interactions are taken into account, the nonparametric random survival forest (RSF) method determines important variables affecting survival times with high accuracy and speed and also does not need to test the restrictive assumptions. © 2020, Tehran University of Medical Sciences. All rights reserved.
Related Docs
View other Related Docs
1. An Ensemble Data Mining Approach to Discover Medical Patterns and Provide a System to Predict the Mortality in the Icu of Cardiac Surgery Based on Stacking Machine Learning Method, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (2023)
2. Investigating the Performance of Machine Learning Algorithms in Predicting the Survival of Covid-19 Patients: A Cross Section Study of Iran, Health Science Reports (2023)
3. Predicting Covid-19 Severity in Pediatric Patients Using Machine Learning: A Comparative Analysis of Algorithms and Ensemble Methods, Scientific Reports (2025)
Experts (# of related papers)
View all Related Experts
Alireza Atashi (3)
Kaveh Hosseini (3)
Other Related Docs
4. Machine Learning Approaches to Predict the Need For Intensive Care Unit Admission Among Iranian Covid-19 Patients Based on Icd-10: A Cross-Sectional Study, Health Science Reports (2024)
5. Machine Learning Algorithms for Predicting Mortality After Coronary Artery Bypass Grafting, Frontiers in Cardiovascular Medicine (2022)
6. The Application of Random Forest-Based Models in Prognostication of Gastrointestinal Tract Malignancies: A Systematic Review, Frontiers in Artificial Intelligence (2025)
7. Machine Learning-Based Predictive Modeling of Angina Pectoris in an Elderly Community-Dwelling Population: Results From the Pocosteo Study, PLOS ONE (2025)
8. Optimizing Cardiovascular Disease Mortality Prediction: A Super Learner Approach in the Tehran Lipid and Glucose Study, BMC Medical Informatics and Decision Making (2024)
9. Design and Implementation of an Intelligent Clinical Decision Support System for Diagnosis and Prediction of Chronic Kidney Disease, Koomesh (2022)
10. Development of Glaucoma Predictive Model and Risk Factors Assessment Based on Supervised Models, BioData Mining (2021)
11. Machine Learning-Based Overall Survival Prediction in Gbm Patients Using Mri Radiomics, 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium# Medical Imaging Conference and Room Temperature Semiconductor Detector Conference (2022)
12. Prediction of Neonatal Deaths in Nicus: Development and Validation of Machine Learning Models, BMC Medical Informatics and Decision Making (2021)
13. A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset, IEEE Access (2020)
14. Machine Learning-Based Prediction of 1-Year Mortality in Hypertensive Patients Undergoing Coronary Revascularization Surgery, Clinical Cardiology (2023)
15. Prediction of Breast Cancer Using Machine Learning Approaches, Journal of Biomedical Physics and Engineering (2022)
16. Prediction of Subsequent Fragility Fractures: Application of Machine Learning, BMC Musculoskeletal Disorders (2024)
17. Prediction of the Fatal Acute Complications of Myocardial Infarction Via Machine Learning Algorithms, Journal of Tehran University Heart Center (2023)
18. Cardiopulmonary Resuscitation of Infants at Birth: Predictable or Unpredictable?, Acute and Critical Care (2022)
19. Machine Learning Prediction of One-Year Mortality After Percutaneous Coronary Intervention in Acute Coronary Syndrome Patients, International Journal of Cardiology (2024)
20. Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (Facs), Endocrinology, Diabetes and Metabolism (2025)