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Waist circumference prediction for epidemiological research using gradient boosted trees.

Weihong Zhou1, Spencer Eckler2, Andrew Barszczyk3

  • 1Department of Health Management Centre, Drum Tower Hospital Affiliated to Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, China.

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|March 22, 2021
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Advanced machine learning models can accurately predict waist circumference using standard physical features. This approach offers a valuable tool for health research when direct measurements are unavailable or inaccurate.

Keywords:
Gradient boosted treesMachine learningMultilayer perceptronWaist circumference

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Area of Science:

  • Biometrics
  • Machine Learning
  • Public Health

Background:

  • Waist circumference is a key health risk predictor.
  • Clinical datasets often lack waist circumference data.
  • Self-reported waist circumference can be inaccurate.

Purpose of the Study:

  • To assess XGBoost's ability to predict waist circumference from standard physical features.
  • To compare XGBoost model performance against linear regression.
  • To determine the importance of individual features in prediction.

Main Methods:

  • Trained XGBoost and linear regression models on 60,740 participants.
  • Used height, weight, BMI, age, race/ethnicity, and sex as predictors.
  • Employed 10 iterations of 90% training and 10% testing data splits.
  • Externally validated the top-performing model.

Main Results:

  • XGBoost models accurately predicted waist circumference (mean bias ± SD: 0.0 ± 0.04 cm, RMSE: 4.7 ± 0.05 cm).
  • Top predictors included Body Mass Index, weight, and Asian race.
  • External validation showed population-specific over/underestimation (UK: +4.65 cm, China: -1.7 cm).

Conclusions:

  • XGBoost models provide accurate waist circumference predictions from readily available physical features.
  • This machine learning approach is highly valuable for epidemiological studies and clinical research.
  • The findings support the use of predictive modeling to overcome data gaps in waist circumference measurement.