Development of physical fitness prediction models for Turkish secondary school students using machine learning methods

Physical fitness is a necessary component for daily activities and has significant effect on our bodies. Because of this importance of physical fitness, maintenance of physical fitness is essential for health and well-being. Physical fitness is a set of attributes that are either health or skill-related, which can be measured with specific tests, and maintaining is essential for the quality of life. However, there are certain difficulties associated with the direct measurement of physical fitness such as the high cost of equipment, availability of experienced staff and the long assessment time. Since the measurement of physical activity has a key role in performing physical fitness rate, researchers need different ways to determine physical fitness. The aim of this thesis is to develop new prediction models for Turkish secondary school students by using machine learning methods including Support Vector Machines (SVM), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN), and Single Decision Tree (SDT). The performance of the SVM-based, GRNN-based, RBFNN-based and SDT-based models have been evaluated by using 10-fold cross-validation and the root means square errors (RMSEs) have been used to compute the errors of prediction. On the scope of the results, this thesis has showed the efficiency of machine-learning methods to indicate high accuracy of the physical fitness prediction.