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Bryan V Phillips-Farfán1

  • 1Laboratorio de Nutrición Experimental, Instituto Nacional de Pediatría. Insurgentes Sur 3700, Letra "C", Alcaldía Coyoacán, CDMX, 04530, Mexico.

Computers in Biology and Medicine
|January 5, 2024
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Summary
This summary is machine-generated.

Machine learning accurately estimates waist circumference (WC) using common data like weight and height, overcoming limitations of body mass index (BMI) for better health risk prediction.

Keywords:
Algorithm selectionExternal cross-validationHyperparameter optimizationMachine-learningModel inspectionRegression

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Area of Science:

  • Health Informatics
  • Biostatistics
  • Machine Learning

Background:

  • Body mass index (BMI) is a common obesity measure but has limitations in predicting health risks.
  • Waist circumference (WC) is a better predictor of obesity-related disease risk.
  • Existing datasets often have incomplete, inaccurate, or missing WC data.

Purpose of the Study:

  • To develop and validate a machine learning model for accurately estimating waist circumference (WC).
  • To utilize readily available predictor variables (weight, height, age, sex) for WC estimation.
  • To provide a reliable method for assessing obesity when WC data is unavailable or unreliable.

Main Methods:

  • Systematic data cleaning, including handling missing values and outliers.
  • Cross-validation of existing regression algorithms to select the best performing model.
  • Hyperparameter optimization and external validation of the selected machine learning model using diverse datasets.
  • Utilized publicly available data, including non-adults, and common predictor variables.

Main Results:

  • The tuned machine learning algorithm accurately estimates WC using limited predictor variables.
  • The model outperforms previous approximations for WC estimation.
  • The approach is effective even with incomplete or unreliable WC measurements in datasets.

Conclusions:

  • Machine learning offers a robust solution for estimating WC, improving upon traditional BMI metrics.
  • This method enhances the utility of existing datasets for health risk assessment.
  • The approach is adaptable for estimating other health-related variables.