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

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

Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we c...

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Bibliographic Details
Main Authors: Clara García-Vicente, David Chushig-Muzo, Inmaculada Mora-Jiménez, Himar Fabelo, Inger Torhild Gram, Maja-Lisa Løchen, Conceição Granja, Cristina Soguero-Ruiz
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Applied Sciences
Subjects:
synthetic categorical data generation
generative adversarial networks
imbalance learning
CTGAN
interpretable machine learning
cardiovascular disease
Online Access:https://www.mdpi.com/2076-3417/13/7/4119

Internet

https://www.mdpi.com/2076-3417/13/7/4119

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