Eyvazlou, Meysam and Hosseinpouri, Madhi and Mokarami, Hamidreza and Gharibi, Vahid and Jahangiri, Mehdi and Cousins, Rosanna and Nikbakht, Hossein-Ali and Barkhordari, Abdullah (2020) Prediction of Metabolic Syndrome based on Sleep and Work-related Risk Factors using an Artificial Neural Network. BMC Endocrine Disorders. (Accepted for Publication)
![[thumbnail of MetS for HIRA.pdf]](https://hira.hope.ac.uk/style/images/fileicons/text.png) |
Text
MetS for HIRA.pdf - Accepted Version Restricted to Repository staff only Download (882kB) | Request a copy |
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 468 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.
| Item Type: | Article |
|---|---|
| Additional Information and Comments: | This will be published Open Access in due course. |
| Keywords: | Metabolic syndrome; work-related stressors; obstructive sleep apnea; workplace; modelling |
| Faculty / Department: | Faculty of Human and Digital Sciences > School of Psychology |
| Depositing User: | Rosanna Cousins |
| Date Deposited: | 26 Oct 2020 15:14 |
| Last Modified: | 26 Oct 2020 15:14 |
| URI: | https://hira.hope.ac.uk/id/eprint/3166 |
Available Versions of this Item
- Prediction of Metabolic Syndrome based on Sleep and Work-related Risk Factors using an Artificial Neural Network. (deposited 26 Oct 2020 15:14) [Currently Displayed]
Actions (login required)
 |
View Item |
Altmetric
Altmetric
