Artificial neural network-based model for predicting VO2max from a submaximal exercise test

Authors:
Mehmet Fatih Akay;Elrasheed Ismail Mohommoud Zayid;Erman Aktürk;James D. George
Affiliations:
Cukurova University, Dept. of Computer Engineering, Adana 01330, Turkey;Cukurova University, Dept. of Electrical and Electronics Engineering, Adana 01130, Turkey;Cukurova University, Dept. of Physics, Adana 01330, Turkey;Brigham Young University, Dept. of Exercise Sciences, Provo, UT 84602, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict maximal oxygen uptake (VO"2max) of fit adults from a single stage submaximal treadmill jogging test. Participants (81 males and 45 females), aged from 17 to 40years, successfully completed a maximal graded exercise test (GXT) to determine VO"2max. The variables; gender, age, body mass, steady-state heart rate and jogging speed are used to build the ANN prediction model. Using 10-fold cross validation on the dataset, the average values of standard error of estimate (SEE), Pearson's correlation coefficient (r) and multiple correlation coefficient (R) of the model are calculated as 1.80mlkg^-^1min^-^1, 0.95 and 0.93, respectively. Compared with the results of the other prediction models in literature that were developed using Multiple Linear Regression Analysis, the reported values of SEE, r and R in this study are considerably more accurate.