Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction
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View abstract on PubMed
Summary
This summary is machine-generated.This study compared survival prediction models for breast cancer progression. Random survival forest and conditional inference forest models outperformed the Cox proportional hazards model in predicting relapse.
Area Of Science
- Oncology
- Biostatistics
- Machine Learning
Background
- Breast cancer is a leading global cancer in women, characterized by significant heterogeneity.
- Despite advancements, a universal treatment remains elusive, necessitating deeper understanding of disease progression.
- Survival data analysis is crucial for navigating the complex trajectories of breast cancer.
Purpose Of The Study
- To evaluate and compare the performance of Cox proportional hazards (PH) model, random survival forest (RSF), and conditional inference forest (Cforest) for breast cancer progression prediction.
- To assess prediction accuracy using bootstrap and bootstrap .632 estimation methods.
- To identify the most effective model for estimating breast cancer patient survival probability.
Main Methods
- Utilized German Breast Cancer Study Group 2 (GBSG2) and METABRIC datasets, focusing on disease relapse and time to relapse.
- Applied Cox PH model, RSF, and Cforest for survival analysis.
- Evaluated model performance using concordance index (C-index) and prediction error curves (pec).
Main Results
- The RSF and Cforest models demonstrated superior performance over the Cox PH model, indicated by higher C-index values and lower prediction errors.
- Both datasets showed consistent results, highlighting the effectiveness of non-parametric approaches.
- The study identified key covariates influencing breast cancer progression.
Conclusions
- RSF and Cforest offer robust non-parametric alternatives to the Cox PH model for predicting breast cancer survival.
- These advanced methods improve the accuracy of breast cancer progression prediction.
- The findings support the use of RSF and Cforest in clinical settings for better patient outcome estimation.
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