Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST.

ABSTRACT:

Objective

Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the possibility of using machine learning to evaluate CPET data for automatically classifying the CPE level of workers in high-latitude areas.

Methods

A total of 120 eligible workers were selected for this cardiopulmonary exercise experiment, and the physiological data and completion of the experiment were recorded in the simulated high-latitude workplace, within which 84 sets of data were used for XGBOOST model training and36 were used for the model validation. The model performance wa

SUBMITTER: Deng J 

PROVIDER: S-EPMC9600669 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature