Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures.

Journal: Clinical nutrition (Edinburgh, Scotland)
PMID: 34879301

Abstract

BACKGROUND & AIMS: Lean muscle and fat mass in the human body are important indicators of the risk of cardiovascular and metabolic diseases. Techniques such as dual-energy X-ray absorptiometry (DXA) accurately measure body composition, but they are costly and not easily accessible. Multiple linear regression (MLR) models have been developed to estimate body composition using simple demographic and anthropometric measures instead of expensive techniques, but MLR models do not explore nonlinear interactions between inputs. In this study, we developed simple demographic and anthropometric measure-driven artificial neural network (ANN) models that can estimate lean muscle and fat mass more effectively than MLR models.

Authors

  • Prabhat Pathak
    Department of Physical Education, Seoul National University, Republic of Korea.
  • Siddhartha Bikram Panday
    Department of Sports and Leisure Studies, Keimyung University, Republic of Korea.
  • Jooeun Ahn
    Department of Physical Education, Seoul National University, Republic of Korea; Institute of Sport Science, Seoul National University, Republic of Korea. Electronic address: ahnjooeun@snu.ac.kr.

Keywords

External Resources

Popular Topics
Recent Journals