Diagnostic performance of an artificial neural network to predict excess body fat in children

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.