Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

Background There is currently no consensus on the impact of class imbalance methods on the performance of clinical prediction models. We aimed to empirically investigate the impact of random oversampling and random undersampling, two commonly used class imbalance methods, on the internal and externa...

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Bibliographic Details
Published in:	Journal of big data Vol. 11; no. 1; pp. 7 - 17
Main Authors:	Yang, Cynthia, Fridgeirsson, Egill A., Kors, Jan A., Reps, Jenna M., Rijnbeek, Peter R.
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2024 Springer Nature B.V SpringerOpen
Subjects:	Big Data Calibration Class Imbalance Problem Classifiers Clinical decision support Clinical prediction model Communications Engineering Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Database Management Discrimination External validation Health Health information Imbalance Information Storage and Retrieval Machine learning Mathematical Applications in Computer Science Networks Oversampling Patient-level prediction Performance prediction Prediction models Validity Patient-level prediction Clinical prediction model Class Imbalance Problem Clinical decision support External validation Machine learning
ISSN:	2196-1115, 2196-1115
Online Access:	Get full text
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