Classification of Impaired Waist to Height Ratio Using Machine Learning Technique

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Emerging Trends and Technologies - Proceedings of ICAETT 2020
EditoresLap-Kei Lee, Leong Hou U, Fu Lee Wang, Simon K. Cheung, Oliver Au, Kam Cheong Li
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas12
ISBN (versión impresa)9783030636647
EstadoPublicada - 2021
Evento2nd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2020 - Riobamba, Ecuador
Duración: 26 oct 202030 oct 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365


Conferencia2nd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2020


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