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

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 f...

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Veröffentlicht in:	International journal of environmental research and public health 2020-12, Vol.17 (24), p.9288
Hauptverfasser:	Wang, Feng-Hsu, Lin, Chih-Ming
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Antihypertensives Area Under Curve Back propagation Blood pressure Cardiovascular disease Datasets Diagnostic systems Disease prevention Female Health care Health promotion Humans Life Style Lifestyles Male Mass Screening - methods Medical personnel Medical screening Metabolic disorders Metabolic syndrome Metabolic Syndrome - diagnosis Metabolic Syndrome - epidemiology Middle Aged Neural networks Neural Networks, Computer Predictions Questionnaires Risk factors Sampling Sampling methods Sampling techniques Social Class Socioeconomic factors Socioeconomics Taiwan - epidemiology Time measurement Variables Waist Circumference Womens health
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