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

Bibliographic Data

ID15484875
AuthorsFeng-Hsu Wang (0000-0002-9553-1212, Ming Chuan University), Chih‐Ming Lin (0000-0001-9861-9060, corresponding author), Chih-Ming Lin (0000-0001-9637-395X, Ming Chuan University, corresponding author)
Year2020
Volume17
Issue24
Pages9288-9288
Publication date2020-12-11
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17249288
PMID33322521
OpenAlexW3113268291
LanguageEN
Citations received1
References cited21

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

Artificial neural network · Environmental health · Machine learning · Metabolic syndrome · Obesity · Physical therapy · Population · Socioeconomic status · Test (biology · Waist · Artificial Intelligence in Healthcare · Computer Science · Diabetes, Cardiovascular Risks, and Lipoproteins · Health Promotion and Cardiovascular Prevention · Medicine · Gerontology · Internal Medicine

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