Abstract
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 \(BMI\ge \) 25 Kg/m\(^2\), and obesity with a \(BMI\ge \) 30 kg/m\(^2\). 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 (\(AUC_{ROC}\)), 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 \(AUC_{ROC}=0.932\) (\(SEN=0.871\), \(SPE=0.855\), \(PPV=0.536\) 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altuve, M., Severeyn, E., Wong, S.: Adaptation of five indirect insulin sensitivity evaluation methods to three populations: Metabolic syndrome, athletic and normal subjects. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4555–4558 (2014)
Altuve, M., Severeyn, E., Wong, S.: Unsupervised subjects classification using insulin and glucose data for insulin resistance assessment. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–7 (2015)
Anoop, S., Misra, A., Bhatt, S.P., Gulati, S., Mahajan, H., Prabakaran, G.: High plasma glucagon levels correlate with waist-to-hip ratio, suprailiac skinfold thickness, and deep subcutaneous abdominal and intraperitoneal adipose tissue depots in nonobese Asian Indian males with type 2 diabetes in North India. J. Diab. Res. 2017 (2017)
Anusruti, A., Jansen, E.H., Gao, X.Y., Xuan, H.B., Schoettker, B.: Longitudinal associations of body mass index, waist circumference, and waist-to-hip ratio with biomarkers of oxidative stress in older adults: results of a large cohort study. Obes. Facts 13(1), 66–76 (2020)
Aschner, P., Buendía, R., Brajkovich, I., Gonzalez, A., Figueredo, R., Juarez, X.E., Uriza, F., Gomez, A.M., Ponte, C.I.: Determination of the cutoff point for waist circumference that establishes the presence of abdominal obesity in Latin American men and women. Diabet. Res. Clin. Pract. 93(2), 243–247 (2011)
Ashwell, M., Gunn, P., Gibson, S.: Waist-to-height ratio is a better screening tool than waist circumference and bmi for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Rev. 13(3), 275–286 (2012)
Bener, A., Yousafzai, M.T., Darwish, S., Al-Hamaq, A.O., Nasralla, E.A., Abdul-Ghani, M.: Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio. J. Obes. 2013 (2013)
Bomberg, E., Birch, L., Endenburg, N., German, A., Neilson, J., Seligman, H., Takashima, G., Day, M.: The financial costs, behaviour and psychology of obesity: a one health analysis. J. Comp. Pathol. 156(4), 310–325 (2017)
Bratke, H., Bruserud, I.S., Brannsether, B., Abmus, J., Bjerknes, R., Roelants, M., Juliusson, P.: Timing of menarche in norwegian girls: associations with body mass index, waist circumference and skinfold thickness. BMC Pediatr. 17(1), 138 (2017)
Castro, A.V.B., Kolka, C.M., Kim, S.P., Bergman, R.N.: Obesity, insulin resistance and comorbidities? mechanisms of association. Arq. Bras. Endocrinol. Metabologia 58(6), 600–609 (2014)
Cheng, C.H., Ho, C.C., Yang, C.F., Huang, Y.C., Lai, C.H., Liaw, Y.P.: Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population. Nutr. Res. 30(9), 585–593 (2010)
Cheng, Y.H., Tsao, Y.C., Tzeng, I.S., Chuang, H.H., Li, W.C., Tung, T.H., Chen., J.Y.: Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese. Medicine 96(39), e8126 (2017)
Herrera, H., Rebato, E., Arechabaleta, G., Lagrange, H., Salces, I., Susanne, C.: Body mass index and energy intake in venezuelan university students. Nutr. Res. 23(3), 389–400 (2003)
Huxley, R., Mendis, S., Zheleznyakov, E., Reddy, S., Chan., J.: Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Euro. J. Clin. Nutr. 64(1), 16–22 (2010)
Imai, A., Komatsu, S., Ohara, T., Kamata, T., Yoshida, J., Miyaji, K., Takewa, M., Kodama, K.: Visceral abdominal fat accumulation predicts the progression of noncalcified coronary plaque. Atherosclerosis 222(2), 524–529 (2012)
Kriemler, S., Puder, J., Zahner, L., Roth, R., Meyer, U., Bedogni, G.: Estimation of percentage body fat in 6-to 13-year-old children by skinfold thickness, body mass index and waist circumference. Br. J. Nutr. 104(10), 1565–1572 (2010)
Lê, K.A., Ventura, E.E., Fisher, J.Q., Davis, J.N., Weigensberg, M.J., Punyanitya, M., Hu, H.H., Nayak, K.S., Goran, M.I.: Ethnic differences in pancreatic fat accumulation and its relationship with other fat depots and inflammatory markers. Diab. Care 34(2), 485–490 (2011)
Marusteri, M., Bacarea, V.: Comparing groups for statistical differences: how to choose the right statistical test? Biochem. Medic 20(1), 15–32 (2010)
Mirmiran, P.: Body mass index as a measure of percentage body fat prediction and excess adiposity diagnosis among iranian adolescents. Arch. Iran. Med. 17(6), 400 (2014)
Misra, A., Chowbey, P., Makkar, B.M., Vikram, N.K., Wasir, J.S., Chadha, D., Joshi, S.R.: Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J. Assoc. Phys. India 57(2), 163–170 (2009)
Ng, C., Elliott, M., Riosmenna, F., Cunningham, S.: Beyond recent BMI: BMI exposure metrics and their relationship to health. SSM-Population Health, p. 100547 (2020)
de Oliveira, C.M.C., Kubrusly, M., Mota, R.S., Choukroun, G., Neto, J.B., da Silva, C.A.B.: Adductor pollicis muscle thickness: a promising anthropometric parameter for patients with chronic renal failure. J. Renal Nutr. 22(3), 307–316 (2012)
Reitsma, J.B., Glas, A.S., Rutjes, A.W., Scholten, R.J., Bossuyt, P.M., Zwinderman, A.H.: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 58(10), 982–990 (2005)
Ronnecke, E., Vogel, M., Bussler, S., Grafe, N., Jurkutat, A., Schlingmann, M., Koerner, A., Kiess, W.: Age-and sex-related percentiles of skinfold thickness, waist and hip circumference, waist-to-hip ratio and waist-to-height ratio: results from a population-based pediatric cohort in germany (life child). Obes. Facts 12(1), 25–40 (2019)
Selcuk, A., Bulucu, F., Kalafat, F., Cakar, M., Demirbas, S., Karaman, M., Ay, S.A., Saglam, K., Balta, S., Demirkol, S., Arslan, E.: Skinfold thickness as a predictor of arterial stiffness: obesity and fatness linked to higher stiffness measurements in hypertensive patients. Clin. Exp. Hypertens. 35(6), 459–464 (2013)
Shin, H.Y., Lee, D.C., Chu, S.H., Jeon, J.Y., Lee, M.K., Im, J.A., Lee, J.W.: Chemerin levels are positively correlated with abdominal visceral fat accumulation. Clin. Endocrinol. 77(1), 47–50 (2012)
Trijsburg, L., Geelen, A., Hollman, P.C., Hulshof, P.J., Feskens, E.J., van’t Veer, P., Boshuizen, H.C., de Vries, J.H.: BMI was found to be a consistent determinant related to misreporting of energy, protein and potassium intake using self-report and duplicate portion methods. Public Health Nutr. 20(4), 598–607 (2017)
Velásquez, J., Wong, S., Encalada, L., Herrera, H., Severeyn, E.: Lipid-anthropometric index optimization for insulin sensitivity estimation. In: Romero, E., Lepore, N., García-Arteaga, J.D., Brieva, J. (eds.) 11th International Symposium on Medical Information Processing and Analysis, vol. 9681, pp. 195–204. International Society for Optics and Photonics, SPIE (2015)
Wang, Y., McPherson, K., Marsh, T., Gortmaker, S., Brown, M.: Health and economic burden of the projected obesity trends in the USA and the UK. Lancet (London, Engl.) 378(9793), 815–825 (2011)
Who, E.C.: Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet (London, Engl.) 363(9403), 157 (2004)
Williams, E., Mesidor, M., Winters, K., Dubbert, P., Wyatt, S.: Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Curr. Obes. Rep. 4, 363–370 (2015)
Wyatt, S., Winters, K., Dubbent, P.: Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Am. J. Med. Sci. 331(4), 166–174 (2006)
Yki-Järvinen, H.: Liver fat in the pathogenesis of insulin resistance and type 2 diabetes. Dig. Dis. 28(1), 203–209 (2010)
Acknowledgment
This work was funded by the Research and Development Deanery of the Simón Bolívar University (DID) and the Research Direction of the Ibagué University. Full acknowledgement is given to David Powers, author of “Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation” (BioInfo Publications™).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Severeyn, E., Velásquez, J., Herrera, H., Wong, S., Cruz, A.L. (2021). Analysis of Receiver Operating Characteristic Curve Using Anthropometric Measurements for Obesity Diagnosis. In: Botto-Tobar, M., Zamora, W., Larrea Plúa, J., Bazurto Roldan, J., Santamaría Philco, A. (eds) Systems and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol 1273. Springer, Cham. https://doi.org/10.1007/978-3-030-59194-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-59194-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59193-9
Online ISBN: 978-3-030-59194-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)