Performance comparison of different regression methods for VO2max ESTIMATION [VO2max tahmini için farkli regresyon yöntemlerinin performans Karsilastirmas]
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Tarih
2013
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The purpose of this paper is to develop maximal oxygen uptake (VO 2max) 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 VO2max prediction models perform better than other prediction models.
Açıklama
2013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolat
Anahtar Kelimeler
Artificial Neural Networks, Maximal Oxygen Uptake, Support Vector Regression
Kaynak
2013 21st Signal Processing and Communications Applications Conference, SIU 2013
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N/A
