Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

Fatih Abut, Mehmet Fatih AkayDepartment of Computer Engineering, Çukurova University, Adana, TurkeyAbstract: Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport...

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Autores principales:	Abut F, Akay MF
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2015
Materias:	Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/cc1eafa5ea1e427783d4c9263d311c8b
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spelling	oai:doaj.org-article:cc1eafa5ea1e427783d4c9263d311c8b2021-12-02T02:10:16ZMachine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances1179-1470https://doaj.org/article/cc1eafa5ea1e427783d4c9263d311c8b2015-08-01T00:00:00Zhttp://www.dovepress.com/machine-learning-and-statistical-methods-for-the-prediction-of-maximal-peer-reviewed-article-MDERhttps://doaj.org/toc/1179-1470Fatih Abut, Mehmet Fatih AkayDepartment of Computer Engineering, Çukurova University, Adana, TurkeyAbstract: Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.Keywords: machine learning methods, maximal oxygen consumption, prediction models, feature selectionAbut FAkay MFDove Medical PressarticleMedical technologyR855-855.5ENMedical Devices: Evidence and Research, Vol 2015, Iss default, Pp 369-379 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical technology R855-855.5
spellingShingle	Medical technology R855-855.5 Abut F Akay MF Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
description	Fatih Abut, Mehmet Fatih AkayDepartment of Computer Engineering, Çukurova University, Adana, TurkeyAbstract: Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.Keywords: machine learning methods, maximal oxygen consumption, prediction models, feature selection
format	article
author	Abut F Akay MF
author_facet	Abut F Akay MF
author_sort	Abut F
title	Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
title_short	Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
title_full	Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
title_fullStr	Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
title_full_unstemmed	Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
title_sort	machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances
publisher	Dove Medical Press
publishDate	2015
url	https://doaj.org/article/cc1eafa5ea1e427783d4c9263d311c8b
work_keys_str_mv	AT abutf machinelearningandstatisticalmethodsforthepredictionofmaximaloxygenuptakerecentadvances AT akaymf machinelearningandstatisticalmethodsforthepredictionofmaximaloxygenuptakerecentadvances
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Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

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