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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Akay, M.F. Akturk, E. Tuncdemir, A.E. Sen, N.N. |
| Copyright Year | 2013 |
| Description | Author affiliation: Bilgisayar Muhendisligi Bolumu, Cukurova Univ., Adana, Turkey (Akay, M.F.; Akturk, E.) || Matematik-Bilgisayar Bolumu, Cag Univ., Mersin, Turkey (Tuncdemir, A.E.; Sen, N.N.) |
| Abstract | The purpose of this study is to develop new multilayer feed-forward artificial neural network (ANN)-based maximal oxygen uptake $(VO_{2}max)$ prediction models by using submaximal treadmill exercise and nonexercise data. Using 10-fold cross validation on the dataset, standard error of estimate (SEE) and multiple correlation coefficient (R) of the models are calculated. It is shown that the models including submaximal, standard nonexercise and questionnaire variables yield higher R and lower SEE than the ones including submaximal and standard nonexercise variables only. The results of ANN-based models are also compared with the ones obtained by Multiple Linear Regression (MLR) and Support Vector Machines (SVM). It is shown that ANN-based models perform better than MLR and SVM-based models for predicting $VO_{2}max.$ |
| Sponsorship | IEEE Turkey Sect. SP Chapter |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 315186 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467355629 |
| e-ISBN | 9781467355636 |
| e-ISBN | 9781467355612 |
| DOI | 10.1109/SIU.2013.6531406 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-04-24 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Cardiorespiratory Fitness Submaximal Exercise Test Artificial Neural Networks Europe Artificial neural networks Predictive models Educational institutions VO2max Standards |
| Content Type | Text |
| Resource Type | Article |
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