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.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Kupusinac A,Stokić E,Doroslovački Rdoi
10.1016/j.cmpb.2013.10.013subject
Has Abstractpub_date
2014-02-01 00:00:00pages
610-9issue
2eissn
0169-2607issn
1872-7565pii
S0169-2607(13)00355-6journal_volume
113pub_type
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