title: Predicting body fat percentage based on gender, age and BMI by using artificial neural networks.
abstract: In the human body, the relation between fat and fat-free mass (muscles, bones etc.) is necessary for the diagnosis of obesity and prediction of its comorbidities. Numerous formulas, such as Deurenberg et al., Gallagher et al., Jackson and Pollock, Jackson et al. etc., are available to predict body fat percentage (BF%) from gender (GEN), age (AGE) and body mass index (BMI). These formulas are all fairly similar and widely applicable, since they provide an easy, low-cost and non-invasive prediction of BF%. This paper presents a program solution for predicting BF% based on artificial neural network (ANN). ANN training, validation and testing are done by randomly divided dataset that includes 2755 subjects: 1332 women (GEN = 0) and 1423 men (GEN = 1), with AGE from 18 to 88 y and BMI from 16.60 to 64.60 kg/m(2). BF% was estimated by using Tanita bioelectrical impedance measurements (Tanita Corporation, Tokyo, Japan). ANN inputs are: GEN, AGE and BMI, and output is BF%. The predictive accuracy of our solution is 80.43%. The main goal of this paper is to promote a new approach to predicting BF% that has same complexity and costs but higher predictive accuracy than above-mentioned formulas.
authors.affiliation: University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia. Electronic address: sasak@uns.ac.rs.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Kupusinac
authors.affiliation: University of Novi Sad, Medical Faculty, Department of Endocrinology, Diabetes and Metabolic Disorders, Hajduk Veljkova 1, 21000 Novi Sad, Serbia.
authors.forename: Edita
authors.initials: E
authors.lastname: Stokić
authors.affiliation: University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia.
authors.forename: Rade
authors.initials: R
authors.lastname: Doroslovački
mesh_terms: D000273:Adipose Tissue; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012737:Sex Factors; D055815:Young Adult
keywords: Artificial neural networks; Body composition; Body fat percentage; Cardiovascular risk; Obesity