Determination of WHtR Limit for Predicting Hyperglycemia in Obese Persons by Using Artificial Neural Networks
Determination of WHtR Limit for Predicting Hyperglycemia in Obese Persons by Using Artificial Neural Networks
Author(s): Aleksandar Kupusinac, Edith Stokic, Biljana SrdicSubject(s): Electronic information storage and retrieval, Evaluation research, Health and medicine and law
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: Artificial neural networks; obesity; waistto-height ratio
Summary/Abstract: The abdominal obesity is strongly associated with increased risk of obesity-related cardiometabolic disturbances. The proportion of waist circumference and body height, known as waist-to-height ratio (WHtR), has been shown as a good risk indicator related with abdominal obesity. This paper presents a solution based on artificial neural networks(ANN) for determining WHtR limit for predicting hyperglycemia in obese persons. ANN inputs are body mass index (BMI) and glycemia (GLY), and output is waist-to-height ratio (WHtR). ANN training and testing are done by dataset that includes 1281 persons.
Journal: TEM Journal
- Issue Year: 1/2012
- Issue No: 4
- Page Range: 270-272
- Page Count: 3
- Language: English