Maximal oxygen uptake (VO2max) refers to the maximal amount of oxygen that an individual can utilize during intense or maximal exercise. VO2max plays a significant role in sport science, education and research. The direct measurement of VO2max is time-consuming, requires expensive laboratory equipment and trained staff. Because of these disadvantages of direct measurement, numerous VO2max prediction models for a variety of subject groups have been developed. The purpose of this study is to develop new Multiple Linear Regression based on VO2max prediction models for Turkish college students by using physiological and questionnaire variables. The dataset includes the data of 62 (28 females and 34 males) students, ranging in age from 18 to 27 years, from the College of Physical Education and Sports Science at Gazi University. Seven different models consisting of the predictor variables gender, age, weight, height, Perceived Functional Ability scores (PFA-1 and PFA-2), and Physical Activity Rating score (PA-R) have been used to predict VO2max. The performance of the prediction models has been evaluated by calculating their standard error of estimates (SEE's) and multiple correlation coefficients (R's). The prediction model including Gender, Age, Height, Weight, PFA-1 and PAR yields the lowest SEE with 5.14 mL.kg-1.min-1 and highest R with 0.93. It can be concluded that in situations where it is difficult to measure VO2max, the given model with MLR equation can be used to predict the VO2max of college students with acceptable error rates.

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Maximum oxygen consumption (VO2max) is important to observe the endurance of the athletes and evaluate their performance.. Aim is to develop new prediction models for college-aged students using Support Vector Machine (SVM) with Relief-F feature selection algorithm. Ten different models consisting of the predictor variables gender, age, weight, height, maximal heart rate (HRmax), time, speed, Perceived Functional Ability scores (PFA-1 and PFA-2) and Physical Activity Rating score (PA-R) have been created by Relief-F scores for prediction of VO2max. The prediction models’ standard error of estimates (SEE’s) and multiple correlation coefficients (R’s) have been calculated for evaluating their performances. For comparison purposes, Tree Boost (TB) and Radial Basis Function Network (RBFN) based models have also been developed. The results show that the prediction model including PAR, speed, time, weight, PFA-1, gender and HRmax gives the lowest SEE with 6.42 mL.kg−1.min−1 and highest R with 0.79. Also, this study shows that the predictor variables HRmax and gender play a considerable role in VO2max prediction. Keywords: Maximum oxygen uptake, machine learning, feature selection.

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In sports science education and research, the use of artificial intelligence methods along with feature selection algorithms can be of great help for developing prediction models where experimental studies based on measurements are not feasible. In this paper, we present a case study in regards to how sports science can benefit from the use of artificial intelligence methods combined with a feature selection algorithm. More specifically, the purpose of our study is to develop prediction models for upper body power (UBP), which is one of the most important factors affecting the performance of cross-country skiers during races. The dataset, which includes 75 subjects, was obtained from the College of Education, Health and Development of Montana State University. Multilayer Perceptron (MLP) and Single Decision Tree (SDT) along with the minimum-redundancy maximum-relevance (mRMR) feature selection algorithm were used to produce prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60). The predictor variables in the dataset are protocol, gender, age, body mass index (BMI), maximum oxygen uptake (VO2max), maximum heart rate (HRmax), time and heart rate at lactate threshold (HRLT) whereas UBP10 and UBP60 are the target variables. Based on the ranking scores of predictor variables assigned by the mRMR, 16 different prediction models have been developed. By using 10-fold cross-validation, the efficiency of the prediction models has been calculated with their multiple correlation coefficients (R’s) and standard error of estimates (SEE’s). The results show that using less amount of predictor variables than the full set of predictor variables can be useful for prediction of UBP10 and UBP60 with comparable error rates. The model consisting of the predictor variables gender, BMI, VO2max, HRLT and time yields the lowest SEE’s for prediction of UBP10, while the model including the predictor variables gender, age, BMI and VO2max gives the lowest SEE’s for prediction of UBP60, whichever regression method is used. Using these two models instead of the full set of predictor variables yields up to 4.95% and 6.83% decrement rates in SEE’s for MLP and SDT based UBP prediction models, respectively.Keywords: multilayer perceptron; single decision tree; sports science education;