Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Carlos Magno Sousa, Ewaldo Santana, Marcus Vinicius Lopes, Guilherme Lima, Luana Azoubel, Érika Carneiro, Allan Kardec Barros and Nilviane Pires
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Carlos Magno Sousa: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil
Ewaldo Santana: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil
Marcus Vinicius Lopes: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil
Guilherme Lima: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil
Luana Azoubel: Centro de Prevenção de Doenças Renais, University Hospital of Maranhão, São Luís 65080805, MA, Brazil
Érika Carneiro: Centro de Prevenção de Doenças Renais, University Hospital of Maranhão, São Luís 65080805, MA, Brazil
Allan Kardec Barros: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil
Nilviane Pires: Department of Electrical Engineering, Biological Information Processing Lab, Federal University of Maranhão, São Luis 65085680, MA, Brazil

IJERPH, 2019, vol. 16, issue 16, 1-12

Abstract: Background: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents. Methods: The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10–19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio. Results: Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively. Conclusions: The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

Keywords: obesity; adolescent; screening (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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