Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data
In this paper we utilize a survival analysis methodology incorporating Bayesian additive regression trees to account for nonlinear and additive covariate effects. We compare the performance of Bayesian additive regression trees, Cox proportional hazards and random survival forests models for censored survival data, using simulation studies and survival analysis for breast cancer with U.S. SEER database for the year 2005. In simulation studies, we compare the three models across varying sample sizes and censoring rates on the basis of bias and prediction accuracy. In survival analysis for breast cancer, we retrospectively analyze a subset of 1500 patients having invasive ductal carcinoma that is a common form of breast cancer mostly affecting older woman. Predictive potential of the three models are then compared using some widely used performance assessment measures in survival literature.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSurvival AnalysisVariable SelectionSimilar Papers 제목 키워드 기반
Unbiased split variable selection for random survival forests using maximally selected rank statistics
The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for …
Selection biasSurvival PredictionVariable SelectionCopula Entropy based Variable Selection for Survival Analysis
Variable selection is an important problem in statistics and machine learning. Copula Entropy (CE) is a mathematical concept for measuring statistical independence and has been applied to variable selection recently. In …
Survival AnalysisVariable SelectionAutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected va…
BIG-bench Machine LearningInterpretable Machine LearningVariable SelectionReal-time Traffic Accident Risk Prediction based on Frequent Pattern Tree
Traffic accident data are usually noisy, contain missing values, and heterogeneous. How to select the most important variables to improve real-time traffic accident risk prediction has become a concern of many recent stu…
Missing ValuesVariable SelectionUnleashing the Power of Extra-Tree Feature Selection and Random Forest Classifier for Improved Survival Prediction in Heart Failure Patients
Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes. In this study, we ex…
feature selectionSurvival Prediction