New Regression Models for Predicting the Hamstring Muscle Strength using Support Vector Machines

Öz

The purpose of this study is to build new prediction models for estimating the hamstring muscle strength of college-aged athletes using Support Vector Machine (SVM). The dataset is made up of 70 athletes ranging in age from 19 to 38 years who were selected from the College of Physical Education and Sport at Gazi University. The results show that the prediction model including the predictor variables gender, age, height and weight provides a valid and convenient method for estimating hamstring muscle strength within limits of acceptable accuracy. For comparison purposes, prediction models based on Multilayer Perceptron (MLP) and Single Decision Tree (SDT) have also been created, and it is seen that SVM-based models outperforms the MLP-based and SDT-based models for prediction of hamstring muscle strength.

Anahtar Kelimeler

• SVM,MLP,SDT,Hamstring muscle strength,Prediction

Destek Vektör Makinelerini Kullanarak Hamstring Kas Kuvveti Tahmini için Yeni Regresyon Modelleri

Öz

Bu çalışmanın amacı, Destek Vektör Makinesi (DVM) kullanarak üniversite çağındaki sporcuların hamstring kas kuvvetini tahmin etmek için yeni tahmin modelleri oluşturmaktır. Veri seti, yaşları 19 ve 38 arasında değişen, Gazi Üniversitesi Beden Eğitimi ve Spor Yüksekokulu'ndan seçilen 70 sporcudan oluşmaktadır. Elde edilen sonuçlara göre; cinsiyet, yaş, boy ve kilo değişkenlerini içeren tahmin modelinin, kabul edilebilir doğruluk ile hamstring kas kuvvetini tahmin etmek için geçerli ve kullanışlı bir yöntem sağladığını göstermektedir. Karşılaştırma amacıyla, Çok Katmanlı Algılayıcı (ÇKA) ve Tekli Karar Ağacı (TKA) yöntemlerine dayalı tahmin modelleri de oluşturulmuştur ve DVM tabanlı modellerin, hamstring kas gücünün tahmininde ÇKA ve TKA tabanlı modellerden daha iyi performans sergilediği görülmüştür.

Anahtar Kelimeler

• DVM,ÇKA,TKA,Hamstring kas kuvveti,Tahmin

Kaynakça

1. 1. Dervišević, E., Hadžić, V., 2012. Quadriceps and Hamstrings Strength in Team Sports: Basketball, Football and Volleyball, Isokinetics and Exercise Science, vol. 20, no. 4, pp. 293–300.
2. 2. Ford-Smith, C.D., Wyman, J.F., Elswick, R.K., Fernandez, T., 2001. Reliability of Stationary Dynamometer Muscle Strength Testing in Community-dwelling Older Adults, Archives of Physical Medicine and Rehabilitation, vol. 82, no. 8, pp. 1128–1132.
3. 3. Clarke, H.H., 2013. Comparison of Instruments for Recording Muscle Strength, Research Quarterly, American Association for Health, Physical Education and Recreation, vol. 25, no. 4, pp. 398–411.
4. 4. Montgomery, L.C., Douglass, L.W., Deuster, P.A., 1989. Reliability of an Isokinetic Test of Muscle Strength and Endurance, The Journal of Orthopaedic and Sports Physical Therapy, vol. 10, no. 8, pp. 315–322.
5. 5. Kılınç, B.E., Kara, A., Camur, S., Oc, Y., Celik, H., 2015. Isokinetic Dynamometer Evaluation of the Effects of Early Thigh Diameter Difference on Thigh Muscle Strength in Patients Undergoing Anterior Cruciate Ligament Reconstruction with Hamstring Tendon Graft, Journal of Exercise Rehabilitation, vol. 11, no. 2, pp. 95–100.
6. 6. Akay, M.F., Abut, F., Çetin, E., Yarım, İ., Sow, B., 2015. Data-Driven Modeling of Quadriceps and Hamstring Muscle Strength Using Support Vector Machines, in Third International Symposium on Engineering, Artificial Intelligence & Applications, pp. 2–4.
7. 7. Abut, F., Akay, M.F., 2015. Machine Learning and Statistical Methods for the Prediction of Maximal Oxygen Uptake: Recent Advances, Medical devices, vol. 8, pp. 369–379.
8. 8. Akay, M.F., Abut, F., Özçiloğlu, M., Heil, D., 2016. Identifying the Discriminative Predictors of Upper Body Power of Cross-country Skiers using Support Vector Machines Combined with Feature Selection, Neural Computing and Applications, vol. 27, no. 6, pp. 1785–1796.

9. 9. Acikkar, M., Akay, M.F., Ozgunen, K.T., Aydin, K., Kurdak, S.S., 2009. Support Vector Machines for Aerobic Fitness Prediction of Athletes, Expert Systems with Applications, vol. 36, no. 2, pp. 3596–3602.
10. 10. Hsu, C.W., Chang, C.C., Lin, C.J., 2003. A Practical Guide to Support Vector Classification, Dept. of Computer Science, National Taiwan University, Taipei, 2003.
11. 11. Hiregoudar, S.B., Manjunath, K., Patil, K.S., 2014. A Survey: Research Summary on Neural Networks, International Journal of Research in Engineering and Technology, vol. 3, no. 15, pp. 385–389.
12. 12. Banfield, R.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P., 2007. A Comparison of Decision Tree Ensemble Creation Techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 173–180.