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

Yuntian Liu, Jeph Herrin, Chenxi Huang, Rohan Khera, Lovedeep Singh Dhingra, Weilai Dong, Bobak J. Mortazavi, Harlan M. Krumholz, Yuan Lu

Abstract: 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.