Interpretable Data-Driven Approach Based on Feature Selection Methods and GAN-Based Models for Cardiovascular Risk Prediction in Diabetic Patients. - David Chushig-Muzo; Hugo Calero-Díaz; Francisco J. Lara-Abelenda; Vanesa Gómez-Martínez; Conceição Granja; Cristina Soguero-Ruíz (IEEE Access, 2024)

Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability. - Vanesa Gómez-Martínez; David Chushig-Muzo; Marit B. Veierød; Conceição Granja; Cristina Soguero-Ruíz (BioData Min., 2024)

LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks. - Vanesa Gómez-Martínez; Francisco J. Lara-Abelenda; Pablo Peiro-Corbacho; David Chushig-Muzo; Conceição Granja; Cristina Soguero-Ruíz (CoRR, 2024)

Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction. - Francisco J. Lara-Abelenda; David Chushig-Muzo; Pablo Peiro-Corbacho; Vanesa Gómez-Martínez; Ana M. Wägner; Conceição Granja; Cristina Soguero-Ruíz (J. Biomed. Informatics, 2025)