Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction.

Journal: Journal of biomedical informatics
PMID: 40209918

Abstract

OBJECTIVE: Machine learning (ML) models have been extensively used for tabular data classification but recent works have been developed to transform tabular data into images, aiming to leverage the predictive performance of convolutional neural networks (CNNs). However, most of these approaches fail to convert data with a low number of samples and mixed-type features. This study aims: to evaluate the performance of the tabular-to-image method named low mixed-image generator for tabular data (LM-IGTD); and to assess the effectiveness of transfer learning and fine-tuning for improving predictions on tabular data.

Authors

  • Francisco J Lara-Abelenda
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: francisco.lara@urjc.es.
  • David Chushig-Muzo
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: david.chushig@urjc.es.
  • Pablo Peiro-Corbacho
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: pablo.peiro@urjc.es.
  • Vanesa Gómez-Martínez
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: vanesa.gomez@urjc.es.
  • Ana M Wägner
    Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. Electronic address: ana.wagner@ulpgc.es.
  • Conceição Granja
    Norwegian Centre for E-health Research, University Hospital of North, Norway, Tromsø, Norway. Electronic address: conceicao.granja@ehealthresearch.no.
  • Cristina Soguero-Ruiz
    Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain. Electronic address: cristina.soguero@urjc.es.

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