A non-exercise based V02max prediction using FRIEND dataset with a Neural Network Henriques, J., Carvalho, P., Rocha, T., Paredes, S., Cabiddu, R., Trimer, R., Mendes, R., Borghi-Silva, A., Kaminsky, L., Ashley, E., Arena, R., Myers, J., IEEE IEEE. 2017: 4203–6


The main goal of this work is the development of models, based on computational intelligence techniques, in particular neural networks, to predict the maximum oxygen consumption value. While the maximum oxygen consumption is a direct mark of the cardiorespiratory fitness, several studies have also confirmed it also as a powerful predictor of risk for adverse outcomes, such as hypertension, obesity, and diabetes. Therefore, the existence of simpler and accurate models, establishing an alternative to standard cardiopulmonary exercise tests, with the potential to be employed in the stratification of the general population in daily clinical practice, would be of major importance. In the current study, different models were implemented and compared: 1) the traditional Wasserman/Hansen equation; 2) linear regression and; 3) non-linear neural networks. Their performance was evaluated based on the "FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base" [1] being, in the present study, a subset of 12262 individuals employed. The accuracy of the models was performed through the computation of sensitivity and specificity values. The results show the superiority of neural networks in the prediction of maximum oxygen consumption.

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