Using an improved relative error support vector machine for body fat prediction.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 15, 2020

Abstract

BACKGROUND AND OBJECTIVE: The term 'obesity' refers to excessive body fat, and it is a chronic disease associated with various complications. Although a range of techniques for body fat estimation have been developed to assess obesity, they are typically associated with high-cost tests requiring special equipment. Accurate prediction of the body fat percentage based on easily accessed body measurements is thus important for assessing obesity and its related diseases. This paper presents an improved relative error support vector machine approach to predict body fat in a cost-effective manner.

Authors

  • Raymond Chiong
    School of Design, Communication and Information Technology, The University of Newcastle, Callaghan, NSW 2308, Australia.
  • Zongwen Fan
    School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia. Electronic address: zongwen.fan@uon.edu.au.
  • Zhongyi Hu
    College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China.
  • Fabian Chiong
    Alice Springs Hospital, The Gap, NT 0870, Australia. Electronic address: fabian.chiong@nt.gov.au.

Keywords

External Resources

Popular Topics
Recent Journals