Articles liés à Body composition prediction by multivariate statistical...

Body composition prediction by multivariate statistical modeling - Couverture souple

Tian, Simiao

 
9783639862157: Body composition prediction by multivariate statistical modeling
Couverture souple
ISBN 10 : 3639862155 ISBN 13 : 9783639862157
Editeur : Scholars' Press, 2016

Synopsis

The assessment of human body composition (BC) is important for evaluating health and nutritional status. Increased fat mass is associated with an increased risk of metabolic diseases; declined lean mass or fat-free mass is also directly related to health and particularly with the mortality rate. More importantly, reduction in lean mass occurs together with an increase of body fat during aging; therefore assessing these changes in BC may be important because the study will lead to a pre-diagnosis for the prevention of morbidity and mortality risk. Accurate BC measurements can be obtained from DXA and others, but their applications require fixed equipment and are time consuming. Therefore, they are not convenient for routine clinical examinations. Potential uses of statistical methods for BC assessment have been highlighted, we presented in this book a linear and Bayesian network modeling for predicting simultaneously body, trunk and appendicular fat and lean masses based on anthropometric covariables. The main advantages in our proposed multivariate approach consisted in using very simple covariables and enabling to take into account the correlation structure between the response.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The assessment of human body composition (BC) is important for evaluating health and nutritional status. Increased fat mass is associated with an increased risk of metabolic diseases; declined lean mass or fat-free mass is also directly related to health and particularly with the mortality rate. More importantly, reduction in lean mass occurs together with an increase of body fat during aging; therefore assessing these changes in BC may be important because the study will lead to a pre-diagnosis for the prevention of morbidity and mortality risk. Accurate BC measurements can be obtained from DXA and others, but their applications require fixed equipment and are time consuming. Therefore, they are not convenient for routine clinical examinations. Potential uses of statistical methods for BC assessment have been highlighted, we presented in this book a linear and Bayesian network modeling for predicting simultaneously body, trunk and appendicular fat and lean masses based on anthropometric covariables. The main advantages in our proposed multivariate approach consisted in using very simple covariables and enabling to take into account the correlation structure between the response.

Biographie de l'auteur

Ph.D in Applied Statistics, AgroParisTech, France, 2013. From 2014, I'm working at Affiliated Zhongshan Hospital of Dalian University as a Research Fellow. My principal research areas are Bayesian methods (especially Bayesian Networks modeling) applied in clinical setting.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2016
Langue
anglais
ISBN 10 
3639862155
ISBN 13 
9783639862157
Reliure
Broché
Nombre de pages
200
Coordonnées du fabricant
non disponible
Personne responsable
non disponible