Development of New Upper Body Power Prediction Models for Cross-Country Skiers by Using Different Machine Learning Methods
23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Türkiye, 16 - 19 Mayıs 2015, ss.260-263, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası:
- Doi Numarası: 10.1109/siu.2015.7129809
- Basıldığı Şehir: Malatya
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.260-263
- Çukurova Üniversitesi Adresli: Evet
Özet
Upper Body Power (UBP) is one of the most important determinants that directly affects the performance of cross-country skiers during races. In this study, new models have been developed to predict the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using different machine learning methods including Cascade Correlation Network (CCN), Radial Basis Function Neural Network (RBF) and Decision Tree Forest (DTF). The predictor variables used to develop prediction models are age, gender, body mass index (BMI), heart rate (HR), maximal oxygen uptake (VO(2)max) and exercise time. By using 10-fold cross-validation on the dataset, 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 the CCN-based model including the predictor variables age, gender, BMI and VO(2)max yields the lowest SEE both for the prediction of UBP10 and UBP60.