Impact of censoring proportion on the performance of machine learning and Bayesian survival analysis approaches: Simulated and real data study linked with maternal and child health

This publication has 16 references indexed in Scilit:

• Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia
  2022
• A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
  Scientific Reports, 2020
• Correction to: Enhancing SVM for survival data using local invariances and weighting
  BMC Bioinformatics, 2020
• Machine Learning for Survival Analysis
  ACM Computing Surveys, 2019
• A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index
  Computational and Mathematical Methods in Medicine, 2013
• The comparisons of random survival forests and Cox regression analysis with simulation and an application related to breast cancer
  Expert Systems with Applications, 2009
• Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis
  2008
• Survival Analysis Part I: Basic concepts and first analyses
  British Journal of Cancer, 2003
• CENSORING ISSUES IN SURVIVAL ANALYSIS
  Annual Review of Public Health, 1997
• Regression Models and Life-Tables
  Journal of the Royal Statistical Society Series B: Statistical Methodology, 1972