The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression.

ABSTRACT:

Objective

Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models. We examined the effect of imbalance correction on the performance of logistic regression models.

Material and methods

Prediction models were developed using standard and penalized (ridge) logistic regression under 4 methods to address class imbalance: no correction, random undersampling, random oversampling, and SMOTE. Model performance was evaluated in terms of discrimination, calibration, and classification. Using Monte Carlo simulations, we studied the impact of training set size, number of predictors, and the outcome event fraction. A case study on prediction modeling for ovarian cancer diagnosi

SUBMITTER: van den Goorbergh R 

PROVIDER: S-EPMC9382395 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature