Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models

Abstract Background Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution and is often used for imaging and time series data, but there ar...

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Bibliographic Details

Main Authors:	Dan Liu, Samer El Kababji, Nicholas Mitsakakis, Lisa Pilgram, Thomas D. Walters, Mark Clemons, Gregory R. Pond, Alaa El-Hussuna, Khaled El Emam
Format:	Artigo
Language:	Inglês
Published:	BMC 2025-11-01
Series:	BMC Medical Informatics and Decision Making
Subjects:	Data augmentation Machine learning Generative models Artificial intelligence Data scarcity Synthetic data
Online Access:	https://doi.org/10.1186/s12911-025-03266-3
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