Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.

Journal: Clinical neurology and neurosurgery
PMID: 39892298

Abstract

BACKGROUND: Stroke remains a leading cause of death and disability worldwide, with African populations bearing a disproportionately high burden due to limited healthcare infrastructure. Early prediction and intervention are critical to reducing stroke outcomes. This study developed and evaluated a stroke prediction system using Gated Recurrent Units (GRU), a variant of Recurrent Neural Networks (RNN), leveraging the Afrocentric Stroke Investigative Research and Education Network (SIREN) dataset.

Authors

  • Afeez A Soladoye
    Department of Computer Engineering, Federal University, Oye, Ekiti, Nigeria.
  • David B Olawade
    Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom.
  • Ibrahim A Adeyanju
    Department of Computer Engineering, Federal University, Oye, Ekiti, Nigeria.
  • Onoja M Akpa
    Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria; Division of Epidemiology, Biostatistics and Environmental Health, School of Public Health, University of Memphis, USA.
  • Nicholas Aderinto
    Department of Medicine and Surgery, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Mayowa O Owolabi
    Department of Medicine, University of Ibadan, Ibadan, Nigeria,; Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Oyo, Nigeria; University College Hospital, Ibadan, Nigeria.

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