Article

Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk Factors

2023-02-07

Abstract excerpt

Machine Learning (ML) methods have become important to enhance the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it limits the generalization of these models, and biases the learning algorithms. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predict...

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Literature Corpus work: fe468325-9fa2-5e15-843e-a09233bda553
DOI: 10.20944/preprints202302.0117.v1

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Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk FactorsDOI 10.20944/preprints202302.0117.v1Read

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