Performance comparison of different regression methods for vo(2)max estimation
Yükleniyor...
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The purpose of this paper is to develop maximal oxygen uptake (VO(2)max) models by using different regression methods such as Multilayer Feed-Forward Artificial Neural Networks (MFANN's), Support Vector Regression (SVR), Generalized Regression Neural Networks (GRNN's) and Multiple Linear Regression (MLR). The dataset includes data of 439 subjects and the input variables of the dataset are gender, age, body mass index (BMI), percent body fat (BF), respiratory exchange ratio (RER) from treadmill test, self-reported rating of perceived exertion (RPE) from treadmill test, heart rate (HR) and time to exhaustion from treadmill test. The performance of the models is evaluated by calculating their standard error of estimates (SEE) and multiple correlation coefficients (R). The results suggest that MFANN-based VO(2)max prediction models perform better than other prediction models.
Açıklama
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
WOS: 000325005300440
WOS: 000325005300440
Anahtar Kelimeler
Artificial Neural Networks, Support Vector Regression, Maximal Oxygen Uptake
Kaynak
2013 21st sıgnal processıng and communıcatıons applıcatıons conference (sıu)
