Support vector machine technique as classifier of impaired body fat percentage

Alexandra La Cruz, Erika Severeyn, Mónica Huerta, Sara Wong

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


Excess weight and obesity are indicators of an unhealthy or harmful accumulation of fat that can be dangerous to health. Body mass index (BMI) refers to height-to-weight radio and is often used to identify overweight and obesity in adults. Although BMI is commonly used to diagnose obesity and overweight, it is ineffective in differentiating between high muscle mass and elevated body fat mass. Body fat percentage (BF%) is one of the best predictors of obesity because it quantifies adipose tissue. The Deurenberg equation is among the indirect methods to measure BF%; it uses BMI, age, and sex as parameters to calculate the BF%. Machine learning techniques demonstrated to be a good classifier of overweight, obesity, and diseases related to insulin resistance and metabolic syndrome. This study intends to evaluate anthropometric parameters as classifiers of BF% alteration using support vector machines and the Deurenberg equation for BF% estimation. The database used consisted of 1978 individuals with 24 different anthropometric measurements. The results suggest the SVM as a suitable technique for classifying individuals with normal and abnormal BF% values. Accuracy, F1 score, PPV, NPV, and sensitivity were above 0.8. Besides, the specificity value is below 0.7, which indicates that false positives may occur. As future work, this research intends to apply neural networks as a classification technique.

Idioma originalInglés
Título de la publicación alojadaFuzzy Systems and Data Mining VII - Proceedings of FSDM 2021
EditoresAntonio J. Tallon-Ballesteros
EditorialIOS Press BV
Número de páginas8
ISBN (versión digital)9781643682143
EstadoPublicada - 14 oct 2021
Evento7th International Conference on Fuzzy Systems and Data Mining, FSDM 2021 - Virtual, Online, República de Corea
Duración: 26 oct 202129 oct 2021

Serie de la publicación

NombreFrontiers in Artificial Intelligence and Applications
ISSN (versión impresa)0922-6389


Conferencia7th International Conference on Fuzzy Systems and Data Mining, FSDM 2021
País/TerritorioRepública de Corea
CiudadVirtual, Online


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