Analysis of Receiver Operating Characteristic Curve Using Anthropometric Measurements for Obesity Diagnosis

Erika Severeyn, Jesús Velásquez, Héctor Herrera, Sara Wong, Alexandra La Cruz

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

Resumen

Today, obesity is a major public health problem. Obesity increases the risk of diabetes, coronary artery disease, stroke, cancer, premature death and contributes substantially the costs to society. Obesity can be diagnosed with body mass index (BMI). According to the World Health Organization, the diagnosis of overweight is made with a BMI≥ 25 Kg/m2, and obesity with a BMI≥ 30 kg/m2. The diagnosis of obesity has been made using the abdominal circumference, the hip circumference, the thickness of the skin folds and the percentage of body fat (measured directly or indirectly). Besides, the characteristic operating receiver curves (ROC) have been used to find the optimal cut-off points of hip and waist circumference for the diagnosis of obesity. The aim of this study is to evaluate the ability of anthropometric measures for diagnosing overweight and obesity. A database of 1053 subjects with 26 anthropometric measurements was used. For evaluating the predictive ability of anthropometric measures, the area under the ROC curve (AUCROC), the sensitivity (SEN), the specificity (SPE), the negative predictive value (NPV) and the positive predictive value (PPV) were calculated. The hip circumference was the anthropometric value that best detected overweight/obese subjects with a AUCROC= 0.932 (SEN= 0.871, SPE= 0.855, PPV= 0.536 and NPV= 0.972 ) and an optimal cut-off point of 97.2 cm for recognition of obesity. The findings reported in this research suggest that the diagnosis of obesity can be made with anthropometric measurements. In the future, machine learning techniques, such as: k-means, neural networks or support vector machines; will be explored for the detection of overweight and obesity.

Idioma originalInglés
Título de la publicación alojadaSystems and Information Sciences - Proceedings of ICCIS 2020
EditoresMiguel Botto-Tobar, Willian Zamora, Johnny Larrea Plúa, José Bazurto Roldan, Alex Santamaría Philco
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas71-80
Número de páginas10
ISBN (versión impresa)9783030591939
DOI
EstadoPublicada - 2021
Evento1st International Conference on Systems and Information Sciences, ICCIS 2020 - Manta, Ecuador
Duración: 27 jul 202029 jul 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1273 AISC
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia1st International Conference on Systems and Information Sciences, ICCIS 2020
País/TerritorioEcuador
CiudadManta
Período27/07/2029/07/20

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