Boubacar SOW, Mehmet Fatih AKAY, FATİH ABUT, Ebru ÇETİN, İmdat YARIM, Hacer ALAK

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

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

Destek Vektör Makinelerini Kullanarak Hamstring Kas Kuvveti Tahminiiçin Yeni Regresyon Modelleri

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

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