Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.

ABSTRACT:

Objective

Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys.

Materials and methods

We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES). Maximal oxygen uptake (VO2 max), measured through a submaximal exercise test, served as the gold standard measure for CRF in this study. We applied multiple ML algorithms to build 2 models: a parsimonious model using commonly available interview and examination data, and an extended model additionally incorporating variables from Dual-Energy

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10114129 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature