background: kidney transplantation is the best alternative treatment for end-stage renal disease. several studies have been devoted to investigate predisposing factors of graft rejection. however, there is inconsistency between the results. the objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (rsf), and to identify im...

Background:Dialysis is a process for eliminating extra uremic fluids of patients with chronic renal failure. The present study aimed to determine the variables that influence the survival of dialysis patients using random survival forest model (RSFM) in low-dimensional data with low events per variable (EPV). Methods:In this historical cohort study, infor...

Background and objectives: Application of statistical machine learning methods such as ensemble based approaches in survival analysis has been received considerable interest over the past decades in time-to-event data sets. One of these practical methods is survival forests which have been developed in a variety of contexts due to their high precision, non-parametric and non-linear nature. This...

Background and Objectives: In survival analysis, using the Cox model to determine the effective factors requires the assumptions whose failure of leads to biased results. The aim of this paper was to determine the factors affecting the survival of metastatic gastric cancer patients using the non-parametric method of Randomized Survival Forest (RSF) model and to compare its result with the Cox m...

BACKGROUND Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (...

Recently, rotation forest has been extended to regression and survival analysis problems. However, due to intensive computation incurred by principal component analysis, rotation forest often fails when high-dimensional or big data are confronted. In this study, we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace, bagging and rotation fo...

In the real world, it is very difficult for fish farmers to select perfect species aquaculture in a specific aquatic environment. The main goal of this research build machine learning that can predict an paper, we have utilized model using random forest (RF). To validate model, used dataset environment 11 different fishes. species, characteristics including pH, temperature, and turbidity. As pe...