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

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PREDICTING MAXIMAL OXYGEN UPTAKE USING MULTILAYER
PERCEPTRON AND SPORTS DATA

M. Fatih AKAY , Ebru ÇETIN , Imdat YARIM , Sevtap ERDEM , M. Mikail ÖZÇILOĞLU 3
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1 Department of Computer Engineering, Çukurova University, Adana, Turkey
2 School of Physical Education and Sport, Gazi University, Ankara, Turkey
3 Department of Electrical Electronics Engineering, Kilis 7 Aralık University, Kilis, Turkey

Maximal oxygen uptake (VO max) plays an important role for both sport and medical sciences in different pur-
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poses, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a
person. Due to several disadvantages of direct measurement of VO max, several VO max prediction models have been
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proposed in literature. The purpose of this paper is to develop new VO max prediction models for Turkish college
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students using Multilayer Perceptron (MLP) and sports data. The dataset includes data of 98 subjects and includes
the predictor variables gender, age, height, weight and questionnaires regarding sports ability, history and level. Seven
VO max prediction models have been developed by utilizing the combinations of the sports variables. To compare
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the performance of MLP based models, prediction models based on Radial Basis Function Neural Network (RBFNN)
have also been developed. Standard Error of Estimate (SEE) has been used to assess the performance of all prediction
models. The results show that MLP based prediction models outperform RBFNN based prediction models.
Keywords: Multilayer perceptron; Maximal oxygen uptake; Prediction.

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