Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/7335
Nаziv: | Predicting body fat percentage based on gender, age and BMI by using artificial neural networks | Аutоri: | Aleksandar Kupusinac Edita Stokić Rade Doroslovački |
Ključnе rеči: | Artificial neural networks;Body composition;Body fat percentage;Cardiovascular risk;Obesity | Dаtum izdаvаnjа: | 1-феб-2014 | Čаsоpis: | Computer Methods and Programs in Biomedicine | Sažetak: | 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/m2. 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. © 2013 Elsevier Ireland Ltd. | URI: | https://open.uns.ac.rs/handle/123456789/7335 | ISSN: | 1692607 | DOI: | 10.1016/j.cmpb.2013.10.013 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
Prikаzаti cеlоkupаn zаpis stаvki
SCOPUSTM
Nаvоđеnjа
38
prоvеrеnо 10.05.2024.
Prеglеd/i stаnicа
32
Prоtеklа nеdеljа
7
7
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.