Title :
Development of new non-exercise maximum oxygen uptake models by using different machine learning methods
Author :
Genc, Esin ; Akay, M. Fatih
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Cukurova Univ., Adana, Turkey
Abstract :
Maximal oxygen consumption (VO2max) is the highest amount of oxygen used by the body during intense exercise. In this study, new non-exercise models have been developed by using different machine learning methods for predicting the VO2max values of healthy individuals aged between 18 and 65 years. The models include the non-exercise physiological variables (gender, age, weight and height) and questionnaire data. Cascade Correlation Network (CCN), Group Method of Data Handling (GMDH), Decision Tree Forest (DTF) and Single Decision Tree (SDT) methods have been used for developing the prediction models. The performance of the prediction models has been evaluated by calculating their multiple correlation coefficient (R) and standard error of estimate (SEE). The results show that CCN-based prediction models yield 24.54% on the average lower SEE´s than the ones obtained by other methods.
Keywords :
decision trees; identification; learning (artificial intelligence); oxygen; CCN; CCN-based prediction models; GMDH; SEE; VO2max values; cascade correlation network; decision tree forest; group method of data handling; healthy individuals; machine learning method; maximal oxygen consumption; nonexercise maximum oxygen uptake model; prediction models; single decision tree; standard error of estimate; Decision trees; Impedance; Mathematical model; Oxygen; Physiology; Predictive models; Support vector machines; machine learning; maximum oxygen uptake; regression;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130447