ArticleStatistics in medicine2025
The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: A Simulation Study.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Development and internal validation of a machine learning prediction model for postoperative hemorrhage following modified radical mastectomy for breast cancer.Gland surgery · 2026Article
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- Machine Learning Identifies High-Risk Suicide Profiles in a Population-Based Forensic Registry.Diagnostics (Basel, Switzerland) · 2026Article
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data.Nursing reports (Pavia, Italy) · 2026Article
- Machine Learning Classification of Current Functional Impairment in Older Adults with Diabetes: Evidence from CHARLS 2015.Journal of clinical medicine · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.Journal of clinical medicine · 2026Review
- Predicting unplanned 30-day hospital readmissions in patients with urogenital system disorders: a retrospective EMR-based ensemble learning study in Taiwan.BMC health services research · 2026Observational
- clickBrick prompt engineering: optimizing large language model performance in clinical psychiatry.Npj mental health research · 2026Article
- Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data.JMIR formative research · 2026Article
- Sample size calculation for training ensemble machine learning models on health data.Patterns (New York, N.Y.) · 2026Article
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- On misconceptions about the Brier score in binary prediction models.Global epidemiology · 2026Article
- Article
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- Predicting first-year kidney graft loss at the time of donor offer using machine learning and competing-risk analysis in a Latin American cohort.Clinical kidney journal · 2026Article
- Data-driven identification of outpatient-suitable procedures: a machine learning approach.Health care management science · 2026Article
- Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction.Biomedical engineering online · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionRisk prediction models are increasingly used in healthcare to aid in clinical decision-making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk estimates) is critical. Data available for model development are often not perfectly balanced with the modeled outcome (i.e., individuals with vs. without the event of interest are not equally prevalent in the data). It is common for researchers to correct for class imbalance, yet, the effect of such imbalance corrections on the calibration of machine learning models is largely unknown.
methodsWe studied the effect of imbalance corrections on model calibration for a variety of machine learning algorithms. Using extensive Monte Carlo simulations we compared the out-of-sample predictive performance of models developed with an imbalance correction to those developed without a correction for class imbalance across different data-generating scenarios (varying sample size, the number of predictors, and event fraction). Our findings were illustrated in a case study using MIMIC-III data.
resultsIn all simulation scenarios, prediction models developed without a correction for class imbalance consistently had equal or better calibration performance than prediction models developed with a correction for class imbalance. The miscalibration introduced by correcting for class imbalance was characterized by an over-estimation of risk and was not always able to be corrected with re-calibration.
conclusionCorrecting for class imbalance is not always necessary and may even be harmful to clinical prediction models which aim to produce reliable risk estimates on an individual basis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.