Classification of Impaired Waist to Height Ratio Using Machine Learning Technique

Alexandra La Cruz, Erika Severeyn, Sara Wong, Gilberto Perpiñan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Metabolic dysfunctions are a set of metabolic risk factors that include abdominal obesity, dyslipidemia, insulin resistance, among others. Individuals with any of these metabolic dysfunctions are at high risk of developing type 2 diabetes and cardiovascular disease. Several parameters and anthropometric indices are used to detect metabolic dysfunctions, such as waist circumference and waist-height ratio (WHtR). The WHtR has an advantage over the body mass index (BMI) since the WHtR provides information on the distribution of body fat, particularly abdominal fat. Central fat distribution is associated with more significant cardio-metabolic health risks than total body fat. Machine learning techniques involve algorithms capable of predicting and analyzing data, increasing our understanding of the events being studied. k-means is a clustering algorithm that has been used in the detection of obesity. This research aims to apply the k-means grouping algorithm to study its capability as an impaired WHtR classifier. Accuracy (Acc), recall (Rec), and precision (P) were calculated. A database of 1863 subjects was used; the database consists of fifteen (15) anthropometric variables and two (2) indices; each anthropometric variable was measured for each participant. The results reported in this research suggest that the k-means clustering algorithm is an acceptable classifier of impaired WHtR subjects (Acc= 0.81, P= 0.83, and Rec= 0.73 ). Besides, the k-means algorithm was able to detect subjects with overweight and fatty tissue deposits in the back and arm areas, suggesting that fat accumulation in these areas is directly related to abdominal fat accumulation.

Original languageEnglish
Title of host publicationAdvances in Emerging Trends and Technologies - Proceedings of ICAETT 2020
EditorsLap-Kei Lee, Leong Hou U, Fu Lee Wang, Simon K. Cheung, Oliver Au, Kam Cheong Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages179-190
Number of pages12
ISBN (Print)9783030636647
DOIs
StatePublished - 2021
Event2nd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2020 - Riobamba, Ecuador
Duration: 26 Oct 202030 Oct 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1302
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference2nd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2020
Country/TerritoryEcuador
CityRiobamba
Period26/10/2030/10/20

Keywords

  • Anthropometrics measurements
  • k-means clustering algorithm
  • Waist to height ratio (WHtR)

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