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.

Pediatric Obesity
|December 28, 2018
PubMed

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.
Abstract

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