Implementation of Neural Network-Based Classification Approach on Embedded Platform

Sarić, Rijad; Jokić, Dejan; Beganović, Nejra

doi:10.1007/978-3-030-17971-7_7

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…This algorithm is fast and does not require as much tuning as classic backpropagation [20]. ANN are a powerful tool for recognizing complex functional relationships between covariates and response variables via a learning process [20] and are particularly suitable for prediction of medical diagnoses, including diabetes and pre-diabetes [21][22][23][24][25][26][27]. After training, an ANN system can be applied to predict the output from a given input of new data.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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