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Diagnostic performance of an artificial neural network to predict excess body fat in children
Ibrahim Duran1, Kyriakos Martakis2,3, Mirko Rehberg2
1Center of Prevention and Rehabilitation, UniReha, University of Cologne, Cologne, Germany.
Insights
An artificial neural network (ANN) can better predict excess body fat in children than traditional body mass index (BMI) and waist circumference (WC) measurements. The ANN showed improved diagnostic performance, particularly in boys.
Area of Science:
- Pediatric Endocrinology
- Biostatistics
- Artificial Intelligence in Medicine
Background:
- Childhood obesity is a growing concern, with waist circumference (WC) and body mass index (BMI) z scores commonly used for prediction.
- However, traditional methods like BMI and WC have demonstrated limited sensitivity in accurately identifying excess body fat.
Purpose of the Study:
- To develop and evaluate an artificial neural network (ANN) model for predicting excess body fat in children.
- The ANN model utilizes age, height, weight, and WC as input parameters.
Main Methods:
- Utilized data from the National Health and Nutrition Examination Survey (NHANES) from 1999-2004.
- Included 1999 children aged 8-19 years, with body fat percentage measured via dual energy X-ray absorptiometry (DXA) scans.
- Defined excess body fat as body fat percentage ≥ 85th centile.
Main Results:
- In males, the ANN achieved a sensitivity of 0.795, outperforming BMI (0.721) and WC (0.572) in predicting excess body fat.
- In females, the ANN (0.782) showed comparable performance to BMI (0.751) and significantly outperformed WC (0.523).
Conclusions:
- The ANN model demonstrates superior diagnostic performance for identifying excess body fat in children compared to traditional BMI and WC measures, especially in boys.
- While ANN and BMI z scores performed similarly in girls, both were significantly better than WC z scores.
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.
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