Молимо вас користите овај идентификатор за цитирање или овај линк до ове ставке:
https://open.uns.ac.rs/handle/123456789/7335
Назив: | Predicting body fat percentage based on gender, age and BMI by using artificial neural networks | Аутори: | Aleksandar Kupusinac Edita Stokić Rade Doroslovački |
Кључне речи: | Artificial neural networks;Body composition;Body fat percentage;Cardiovascular risk;Obesity | Датум издавања: | 1-феб-2014 | Часопис: | Computer Methods and Programs in Biomedicine | Сажетак: | 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 |
Налази се у колекцијама: | FTN Publikacije/Publications |
Приказати целокупан запис ставки
SCOPUSTM
Навођења
38
проверено 10.05.2024.
Преглед/и станица
32
Протекла недеља
7
7
Протекли месец
0
0
проверено 10.05.2024.
Google ScholarTM
Проверите
Алт метрика
Ставке на DSpace-у су заштићене ауторским правима, са свим правима задржаним, осим ако није другачије назначено.