Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network
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RESEARCH ARTICLE
Open Access
Prediction of metabolic syndrome based on sleep and work-related risk factors using an artificial neural network Meysam Eyvazlou1 , Mahdi Hosseinpouri2 , Hamidreza Mokarami3 , Vahid Gharibi4* , Mehdi Jahangiri4 , Rosanna Cousins5* , Hossein-Ali Nikbakht6 and Abdullah Barkhordari7
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. Methods: Four hundred sixty-eight employees of an oil refinery in Iran consented to providing anthropometric and biochemical measurements, and survey data pertaining to lifestyle, work-related stressors and sleep variables. National Cholesterol Education Programme Adult Treatment Panel ІІI criteria was used for determining MetS status. The Management Standards Indicator Tool and STOP-BANG questionnaire were used to measure work-related stress and obstructive sleep apnoea respectively. With 17 input variables, multilayer perceptron was used to develop ANNs in 16 rounds of learning. ANNs were compared to logistic regression models using the mean squared error criterion for validation. Results: Sex, age, exercise habit, smoking, high risk of obstructive sleep apnoea, and work-related stressors, particularly Role, all significantly affected the odds of MetS, but shiftworking did not. Prediction accuracy for an ANN using two hidden layers and all available input variables was 89%, compared to 72% for the logistic regression model. Sensitivity was 82.5% for ANN compared to 67.5% for the logistic regression, while specificities were 92.2 and 74% respectively. Conclusions: Our analyses indicate that ANN models which include psychosocial stressors and sleep variables as well as biomedical and clinical variables perform well in predicting MetS. The findings can be helpful in designing preventative strategies to reduce the cost of healthcare associated with MetS in the workplace. Keywords: Metabolic syndrome, Work-related stressors, Obstructive sleep apnea, Workplace, Modelling
* Correspondence: [email protected]; [email protected] 4 Department of Occupational Health, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran 5 Department of Psychology, Liverpool Hope University, Liverpool, UK Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this art
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