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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 24 Sayı: 5

Predicting Binge Eating Disorder Using Machine Learning Methods

Yeme Bozukluklarının Makine Öğrenmesi Yöntemleri Kullanılarak Tahmin Edilmesi

Serel Akyol(Kütahya Sağlık Bilimleri Üniversitesi)0000-0002-5344-4065
Ayşegül Bayramoğlu(Kütahya Sağlık Bilimleri Üniversitesi)0000-0001-8567-807X
Pages
1129–1137
Publication Date
2024-10-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Eating disorders are enduring conditions characterized by elevated rates of mortality and morbidity, presenting a serious threat to life.
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Key Findings & Data:The study was carried out on 306 individuals (117 males, 189 females).
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Conclusion & Impact:This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Makine öğrenmesi algoritmaları · Aşırı yeme bozukluğu · Bulimic investigatory test Edinburgh (BITE)

Abstract

Eating disorders are enduring conditions characterized by elevated rates of mortality and morbidity, presenting a serious threat to life. Among these disorders, binge eating disorder is the most prevalent. Therefore, it is an important health problem that often results in obesity worldwide. This study was conducted to evaluate the eating attitudes and behaviors of university students and predict binge eating disorder using machine learning methods. The study was carried out on 306 individuals (117 males, 189 females). Individuals' personal characteristics were questioned with the questionnaire form. The Bulimic Investigatory Test Edinburgh (BITE) test was used to determine whether individuals taking part in the study had binge eating disorder. In this study, in which binge eating disorder was classified, different artificial neural network models were created by changing the basic parameters, and the optimum model was assessed accordingly. Among the models created with different layers and activation functions, the optimum results were obtained using the number of fully connected layers as 2, first and second layers' sizes as 10, and ReLU, a non-linear activation function, in the Bilayered Neural Network structure. This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.

Özet

Yeme bozuklukları, yüksek ölüm ve hastalık oranlarıyla karakterize deilen ve yaşam için ciddi bir tehdit oluşturan kalıcı durumlardır. Bunlar arasında en yaygın olanı tıkanırcasına yeme bozukluğudur. Bu nedenle dünya çapında sıklıkla obeziteyle sonuçlanan önemli bir sağlık sorunudur. Bu çalışma, üniversite öğrencilerinin yeme tutum ve davranışlarının değerlendirilmesi ve tıkınırcasına yeme bozukluğunun makine öğrenmesi yöntemleri kullanılarak tahmin edilmesi amacıyla yapılmıştır. Araştırma 306 kişi (117 erkek, 189 kadın) üzerinde gerçekleştirilmiştir. Bireylerin kişisel özellikleri anket formu ile sorgulanmıştır. Çalışmaya katılan bireylerde tıkınırcasına yeme bozukluğu olup olmadığını tespit etmek amacıyla, Bulimic Investigatory Test Edinburgh (BITE) testi kullanılmıştır. Tıkınırcasına yeme bozukluğunun tahmin edildiği bu çalışmada, temel parametreler değiştirilerek farklı yapay sinir ağı modelleri oluşturulmuş ve buna göre optimum model değerlendirilmiştir. Farklı katmanlar ve aktivasyon fonksiyonları ile oluşturulan modeller arasında Çift Katmanlı Sinir Ağında tam bağlantılı katman sayısı 2, birinci ve ikinci katman boyutları 10 ve doğrusal olmayan aktivasyon fonksiyonu olan ReLU kullanılarak optimum sonuçlar elde edilmiştir. Bu çalışma, anket çalışmalarından tıkınırcasına yeme bozukluğunun makine öğrenmesi yöntemleri kullanılarak tahmin edildiği ilk çalışma olup, makine öğreniminin, araştırmacıların ve klinisyenlerin yeme bozukluklarının erken teşhisi, önlenmesi ve tedavisine yardımcı olacak önemli bir araç olduğuna inanıyoruz.

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Cite This Article

Akyol, S., Bayramoğlu, A. (2024). Predicting Binge Eating Disorder Using Machine Learning Methods. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 5, 1129–1137. https://doi.org/10.35414/akufemubid.1451334

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