Estimation of Body Fat Percentage Using Support Vector Machine and Random Forest Methods

Year 2021, , 430 - 445, 29.05.2021

Burhan Baraklı , Ahmet Küçüker

https://doi.org/10.29130/dubited.815454

Cited By: 1

Abstract

Obesity is a significant health problem, and its prevalence is increasing. It is known that this disease is the trigger and precursor of many other diseases. Before the treatment of obesity disease, it is important to determine the body fat percentage correctly. Body fat percentage can be measured precisely with high-cost methods. However, these methods are not common to use. In this study, support vector regression and random forest tree regression methods were applied to accurately and cost-effectively estimate body fat percentage with anthropometric data set taken from individuals. In regression methods, model parameter values, number of data, number of features and feature selection are important in prediction performance. In the study based on a 13-featured body fat percentage data set, a new data set was created with 25 statistical methods (skewness, central moment, kurtosis, etc.) frequently used in the literature, and it was observed that the performance of the new data set was higher than other studies in the literature. Estimation accuracy has been increased by determining the regression parameters with grid scanning methods. In addition, features that are highly correlated with body fat percentage were determined with feature reduction methods. It was observed that the prediction success performance of the regression methods performed with the selected features was higher than other similar studies. As the best mean square error values, the value of 2.2519 was obtained in the experiment performed with the Random Forest Trees Method and the new data set created by the statistical method, while the value of 3.174 was reached in the regression experiment with Decision Support Machines and the properties with the best 6 f-score values.

Keywords

Body fat percentage, Support Vector regression, Random forest

Karar Destek Makineleri ve Rastgele Orman Ağaçları Yöntemleri ile Vücut Yağ Yüzdesinin Tahmini

Abstract

Obezite, önemli bir sağlık problemidir ve yaygınlığı giderek artmaktadır. Bu hastalığın, diğer birçok hastalığın tetikleyicisi ve habercisi olduğu bilinmektedir. Obezite hastalığının tedavi sürecinden önce, vücut yağ yüzdesinin doğru bir şekilde tespit edilmesi önemlidir. Yüksek maliyetli yöntemler ile vücut yağ yüzdesi kesin olarak ölçülmektedir. Bu çalışmada, kişilerden alınan antropometrik veri seti ile vücut yağı yüzdesi tespitinin doğru ve maliyetsiz bir şekilde tahmin edilebilmesi için destek vektör regresyonu ile rastgele orman ağaçları regresyon yöntemleri uygulanmıştır. Regresyon yöntemlerinde, model parametre değerleri, veri sayısı, özellik sayısı ve özellik seçimi tahmin başarımında önemlidir. 13 özellikli vücut yağ yüzdesi veri seti baz olarak alınan çalışmada, literatürde sıklıkla kullanılan 25 istatiski yöntem (çarpıklık, merkezi moment, basıklık vb.) ile yeni bir veri seti oluşturulmuş ve elde edilen yeni veri setinin başarım performansının literatürde yer alan diğer çalışmalardan yüksek olduğu görülmüştür. Regresyon parametrelerinin ızgara tarama yöntemleriyle belirlenmesi sayesinde tahmin doğrulukları arttırılmıştır. Ayrıca özellik azaltma yöntemleri ile vücut yağ yüzdesi ile yüksek ilintili özellikler belirlenmiştir. Seçilen özellikler ile gerçekleştirilen regresyon yöntemlerinin tahmin başarı performansının da benzer diğer çalışmalardan yüksek olduğu gözlenmiştir. En iyi ortalama karesel hata değerleri olarak, Rasgele Orman Ağaçları Yöntemi ve istatistiki yöntemle oluşturulan yeni veri seti ile gerçekleştirilen deneyde 2,2519 değeri elde edilirken, Karar Destek Makinaları ve en iyi 6 F-skor değerine sahip özellikler ile yapılan regresyon deneyinde 3,174 değerine ulaşılmıştır.

Keywords

Vücut yağ yüzdesi, Destek vektör regresyonu, Rastgele orman ağaçları regresyonu

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