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Updated: May 30, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: A Simulation Study.

Alex Carriero¹, Kim Luijken¹, Anne de Hond¹

¹Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.

Statistics in Medicine

|January 27, 2025

Summary

This summary is machine-generated.

Correcting for class imbalance in machine learning models may harm calibration. Models without imbalance correction consistently showed better or equal calibration, avoiding risk over-estimation in clinical prediction.

Keywords:

calibration class imbalance machine learning prediction modeling

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Area of Science:

Machine Learning
Biostatistics
Clinical Informatics

Background:

Risk prediction models are vital for clinical decisions, requiring accurate calibration.
Healthcare data often exhibit class imbalance, leading researchers to apply corrections.
The impact of these imbalance corrections on model calibration remains unclear.

Purpose of the Study:

To investigate the effect of class imbalance corrections on the calibration of machine learning models.
To compare calibration performance between models with and without imbalance correction across various scenarios.

Main Methods:

Utilized extensive Monte Carlo simulations to assess out-of-sample predictive performance.
Evaluated machine learning algorithms under different data-generating conditions (sample size, predictors, event fraction).
Illustrated findings with a case study using MIMIC-III data.

Main Results:

Models developed without class imbalance correction consistently demonstrated superior or equivalent calibration.
Imbalance correction led to miscalibration, characterized by risk over-estimation.
Re-calibration did not always resolve the miscalibration introduced by imbalance correction.

Conclusions:

Class imbalance correction is not universally required for clinical prediction models.
Applying imbalance correction can potentially impair model calibration and reliability.
Prioritize model calibration over automatic imbalance correction for individual risk estimation.