ArticleStatistical methods in medical research2025
Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction.
Article in Statistical methods in medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review.Journal of medical Internet research · 2026Article
- Adaptive Gaussian process search for simulation-based sample size estimation in clinical prediction models: validation of the pmsims R package.BMC medical research methodology · 2026Article
- Predicting heterosexual reactions to coming out in China.BMC psychology · 2026Article
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Machine learning techniques (MLTs) are increasingly being used to develop clinical risk prediction models for binary health outcomes but the sample size requirements for developing and validating such models remain unclear. This study investigates whether sample size guidelines that target mean absolute prediction error (MAPE) for logistic regression models can be applied to tree-based ensemble MLTs (bagging, random forests, and boosting). Simulations based on two large cardiovascular datasets were used to evaluate the performance of MLTs in terms of MAPE, calibration, the
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