Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network.

Journal: BMC endocrine disorders
PMID: 33183282

Abstract

BACKGROUND: Metabolic syndrome (MetS) is a major public health concern due to its high prevalence and association with heart disease and diabetes. Artificial neural networks (ANN) are emerging as a reliable means of modelling relationships towards understanding complex illness situations such as MetS. Using ANN, this research sought to clarify predictors of metabolic syndrome (MetS) in a working age population.

Authors

  • Meysam Eyvazlou
    Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
  • Mahdi Hosseinpouri
    Center of Planning, Budgeting and Performance Evaluation, Department of Environment, Tehran, Iran.
  • Hamidreza Mokarami
    Department of Ergonomics, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Vahid Gharibi
    Department of Occupational Health, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran. gharibi@sums.ac.ir.
  • Mehdi Jahangiri
    Department of Occupational Health, Research Center for Health Science, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Rosanna Cousins
    Department of Psychology, Liverpool Hope University, Liverpool, UK. cousinr@hope.ac.uk.
  • Hossein-Ali Nikbakht
    Social Determinants of Health Research Center, Health Research Institute, Department of Biostatistics & Epidemiology, Faculty of Medicine, Babol University of Medical Sciences, Babol, Iran.
  • Abdullah Barkhordari
    Department of Occupational Health, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran.

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