Development of New Non-Exercise Maximum Oxygen Uptake Models by Using Different Machine Learning Methods

Genc E., AKAY M. F.

23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Türkiye, 16 - 19 Mayıs 2015, ss.196-199 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2015.7130447
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.196-199
  • Çukurova Üniversitesi Adresli: Evet

Özet

Maximal oxygen consumption (VO(2)max) 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 VO(2)max 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.