Diagnostic performance of an artificial neural network to predict excess body fat in children. Issue 2 (27th December 2018)

• Record Type: Journal Article
• Authors: Duran, Ibrahim; Martakis, Kyriakos; Rehberg, Mirko; Semler, Oliver; Schoenau, Eckhard
• Is Part Of: Pediatric obesity. Volume 14:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-27
• Subjects: artificial neural network -- body mass index -- diagnostic performance -- excess body fat; Obesity in children -- Periodicals; Obesity in adolescence -- Periodicals; Obesity -- Periodicals; Overweight children -- Periodicals; 618.92398
• DOI: 10.1111/ijpo.12494
• Languages: English
• ISSNs: 1747-7174

Abstract

Summary: Background: Waist circumference (WC) and z scores of body mass index (BMI) are commonly used to predict childhood obesity, although BMI and WC have a limited sensitivity. Objectives: To generate an artificial neural network (ANN), using the input parameters age, height, weight, and WC, to predict excess body fat in children. Methods: As part of the National Health and Nutrition Examination Survey (NHANES) study, in the years 1999 to 2004, the body fat percentage of randomly selected Americans from 8 to 19 years were measured using whole‐body dual energy X‐ray absorptiometry (DXA) scans. Excess body fat was defined as a body fat percentage ≥ 85th centile. Results: The data of 1999 children (856 female) were eligible. In females, the sensitivity of the BMI, WC, and ANN approaches to predict excess body fat were 0.751 (95% CI, 0.730‐0.771), 0.523 (0.487‐0.559), and 0.782 (0.754‐0.810), respectively. In males, the sensitivity of the BMI, WC, and ANN approaches to predict excess body fat were 0.721 (95% CI, 0.699‐0.743), 0.572 (0.549‐0.594), and 0.795 (0.768‐0.821). Conclusions: Only in boys, the diagnostic performance in identifying excess body fat was better by using an ANN than by applying BMI and WC z scores. In girls, the ANN and BMI z scores performed comparable and significantly better than WC z scores.