Why the study?
Do Multilayer Feed-Forward Artificial Neural Networks perform better than other regression methods for estimating VO2max?
Do Multilayer Feed-Forward Artificial Neural Networks perform better than other regression methods for estimating VO2max?
Multilayer Feed-Forward Artificial Neural Networks provide superior performance for estimating VO2max compared to traditional and other machine learning regression methods.
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MFANN may enhance VO2max estimation accuracy; leaves open clinical adoption pending external validation.
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.
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Akturk et al. (2013) studied this question. Multilayer Feed-Forward Artificial Neural Networks (MFANN) vs. Support Vector Regression (SVR), Generalized Regression Neural Networks (GRNN), and Multiple Linear Regression (MLR) was evaluated on Standard error of estimates (SEE) and multiple correlation coefficients (R) for VO2max prediction.
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