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The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics

Author

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  • Feng-Hsu Wang

    (Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan 333, Taiwan)

  • Chih-Ming Lin

    (Department of Healthcare Information and Management, Ming Chuan University, Taoyuan 333, Taiwan)

Abstract

This study investigated the diagnostic accuracy of using an artificial neural network (ANN) for the prediction of metabolic syndrome (MetS) based on socioeconomic status and lifestyle factors. The data of 27,415 subjects who went through examinations and answered questionnaires during three stages from 2006 to 2014 at a health institute in Taiwan were collected and analyzed. The repeated measurements over time were set as predictive factors and used to train and test an ANN for MetS prediction. Among the subjects, 18.3%, 24.6%, and 30.1% were diagnosed with MetS during the respective three stages. ANN analysis applied with an over-sampling technique performed with an area under the curve (AUC) of up to 0.93 based on different models. The over-sampling technique helped improve prediction performance in terms of sensitivity and F 2 measures. The results indicated that waist circumference, socioeconomic status (SES), and lifestyle factors can be utilized in a non-invasive screening tool to assist health workers in making primary care decisions when MetS is suspected. By predicting the occurrence of MetS, individuals or healthcare professionals can then develop preventive strategies in time, thus enhancing the effectiveness of health promotion.

Suggested Citation

  • Feng-Hsu Wang & Chih-Ming Lin, 2020. "The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics," IJERPH, MDPI, vol. 17(24), pages 1-10, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9288-:d:460708
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    References listed on IDEAS

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    1. Xinghua Yang & Qiushan Tao & Feng Sun & Siyan Zhan, 2012. "The impact of socioeconomic status on the incidence of metabolic syndrome in a Taiwanese health screening population," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(3), pages 551-559, June.
    2. Chen-Mao Liao & Chih-Ming Lin, 2018. "Life Course Effects of Socioeconomic and Lifestyle Factors on Metabolic Syndrome and 10-Year Risk of Cardiovascular Disease: A Longitudinal Study in Taiwan Adults," IJERPH, MDPI, vol. 15(10), pages 1-15, October.
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    Cited by:

    1. Laura Zoboroski & Torrey Wagner & Brent Langhals, 2021. "Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use," IJERPH, MDPI, vol. 18(14), pages 1-15, July.

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