Waist circumference prediction for epidemiological research using gradient boosted trees

Waist circumference prediction for epidemiological research using gradient boosted trees

Abstract Background Waist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be...

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Bibliographic Details
Main Authors: Weihong Zhou, Spencer Eckler, Andrew Barszczyk, Alex Waese-Perlman, Yingjie Wang, Xiaoping Gu, Zhong-Ping Feng, Yuzhu Peng, Kang Lee
Format: Article
Language:English
Published: BMC 2021-03-01
Series:BMC Medical Research Methodology
Subjects:
Waist circumference
Machine learning
Gradient boosted trees
Multilayer perceptron
Online Access:https://doi.org/10.1186/s12874-021-01242-9

Internet

https://doi.org/10.1186/s12874-021-01242-9

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