Fatih Abut

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances.

Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.

Development of new upper body power prediction models for cross-country skiers by using different machine learning methods

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 (VO2max) 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 VO2max yields the lowest SEE both for the prediction of UBP10 and UBP60.

Determination of the variables affecting the maximal oxygen uptake of cross-country skiers by using machine learning and feature selection algorithms

Maximal oxygen uptake (VO2max) is one of the most important determinants that directly affects the performance of cross-country skiers during races. In this study, various models have been developed to predict the VO2max of cross-country skiers by combining different machine learning methods with the Relief-F feature selection algorithm. Machine learning methods used in this study include General Regression Neural Network (GRNN), Gene Expression Programming (GEP), Group Method of Data Handling Polynomial Network (GMDH) and Single Decision Tree (SDT). The predictor variables used to develop prediction models are age, gender, weight, height, heart rate (HR), heart rate at lactate threshold (HRLT) 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 GRNN-based model including all predictor variables yields the highest R (0.92) and the lowest SEE (2.98 ml kg-1 min-1).