Page 25 - abstract-Book-version-22.10.18

III. EUROPEAN CONFERENCE ON SCIENCE, ART & CULTURE
ECSAC’18 – NORTHERN CYPRUS
Gazimağusa, October12-14, 2018

PREDICTING PHYSICAL FITNESS USING SUPPORT VECTOR MACHINES OP-19
AND BLOOD TEST RESULTS

M. Fatih AKAY , Ebru ÇETIN , Imdat YARIM , Sevtap ERDEM , Özge BOZKURT 1
1
2
2
1
1 Department of Computer Engineering, Çukurova University, Adana, Turkey
2 School of Physical Education and Sport, Gazi University, Ankara, Turkey
Physical fitness is a state of health and well-being and, more specifically, the ability to perform aspects of sports, occupations and
daily activities. Physical fitness is generally achieved through proper nutrition, moderate-vigorous physical exercise, and sufficient
rest. Measurement of physical fitness requires professional equipment, experienced staff and lots of time. Due to these drawbacks,
researchers require different ways to determine physical fitness. In this paper, we develop new prediction models for predicting the
physical fitness of Turkish secondary school students by using Support Vector Machines (SVM) and blood test results. The dataset
comprises data of 77 subjects and includes the predictor variables gender, age, body mass index (BMI), total cholesterol, levels of he-
moglobin and triglyceride. Seven physical fitness prediction models have been by utilizing the combinations of the blood test results.
To compare the performance of SVM-based models, prediction models based on Radial Basis Function Neural Network (RBFNN)
and Tree Boost (TB) have also been developed. The performance of all prediction models has been calculated by using standard
error of estimate (SEE). The results show that SVM based prediction models outperform other models based on RBFNN and TB.
Keywords: Physical Fitness; Machine Learning; Prediction.

19