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Journals / Acta Medica Nicomedia / 2026 / Cilt: 8 Sayı: 1

Trends and Forecasts of Overweight Prevalence in Türkiye: A Time Series Approach Using ARIMA Models

Türkiye'de Aşırı Kilolu Prevalansının Eğilimleri ve Tahminleri: ARIMA Modellerini Kullanan Bir Zaman Serisi Yaklaşımı

Hülya Özen(SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, GÜLHANE TIP FAKÜLTESİ)0000-0003-4144-3732
Doğukan Özen(ANKARA ÜNİVERSİTESİ, ANKARA VETERİNER FAKÜLTESİ)0000-0003-1943-2690
Pages
52–58
Publication Date
2025-02-28
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AI Summary & Key Takeaways

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

Objective: This study aimed to fit Autoregressive Integrated Moving Average (ARIMA) models to the prevalence of overweight in Türkiye's overall, female, and male populations and to forecast future trends using the best-performing ARIMA models.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: This study aimed to fit Autoregressive Integrated Moving Average (ARIMA) models to the prevalence of overweight in Türkiye's overall, female, and male populations and to forecast future trends using the best-performing ARIMA models.
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Key Findings & Data:Methods: The dataset comprised annual overweight prevalence values for Türkiye's overall, female, and male populations from 1974 to 2022, obtained from the World Health Organization and World Bank Group databases.
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Conclusion & Impact:Conclusion: This study contributes to a foundational understanding of the trajectory of overweight prevalence in Türkiye, providing a basis for evidence-based interventions and long-term health planning.
🔬Design:Modeling & Simulation
📍Scope:2022 Verileri
⚙️Variables:aşırı kilolu prevalansı · ARIMA modelleri · zaman serisi

Abstract

Objective: This study aimed to fit Autoregressive Integrated Moving Average (ARIMA) models to the prevalence of overweight in Türkiye's overall, female, and male populations and to forecast future trends using the best-performing ARIMA models. Methods: The dataset comprised annual overweight prevalence values for Türkiye's overall, female, and male populations from 1974 to 2022, obtained from the World Health Organization and World Bank Group databases. The dataset was divided into training and test sets in a chronological sequence with the ratio 80:20, respectively. Training sets were used to fit ARIMA models, while test sets were used to evaluate the predictive performance of the models. Best ARIMA models were chosen based on various evaluation metrics. Results: The best models were identified as ARIMA (1,3,1) for the overall population, ARIMA (1,3,1) for females, and ARIMA (3,3,1) for males, yielding the lowest error metrics. These models effectively captured the increasing trend in overweight prevalence. Short-term forecasts indicated that the upward trend is likely to continue in the near future. Conclusion: This study contributes to a foundational understanding of the trajectory of overweight prevalence in Türkiye, providing a basis for evidence-based interventions and long-term health planning.

Özet

Amaç: Bu çalışmanın amacı, Türkiye'nin genel, kadın ve erkek nüfusundaki aşırı kilolu prevalansı verilerine Otoregresif Entegre Hareketli Ortalama (ARIMA) modellerini uygulamak ve en iyi performans gösteren ARIMA modellerini kullanarak gelecekteki eğilimleri tahmin etmektir. Yöntem: Çalışmadaki veri seti, Dünya Sağlık Örgütü ve Dünya Bankası Grubu veri tabanlarından elde edilen 1974-2022 yılları arasında Türkiye'nin genel, kadın ve erkek nüfuslarına ait yıllık aşırı kilolu prevalans değerlerinden oluşmaktadır. Veri seti, kronolojik bir sıra ile 80:20 oranında, sırasıyla eğitim ve test setlerine bölünmüştür. Eğitim setleri ARIMA modellerinin oluşturulması için kullanılırken, test setleri modellerin tahmin performansını değerlendirmek için kullanılmıştır. Çeşitli değerlendirme ölçütlerine göre en iyi ARIMA modelleri seçilmiştir. Bulgular: En iyi modeller genel nüfus için ARIMA (1,3,1), kadınlar için ARIMA (1,3,1) ve erkekler için ARIMA (3,3,1) olarak belirlenmiş ve en düşük hata metriklerini vermiştir. Bu modeller aşırı kilolu prevalansındaki artış eğilimini etkili bir şekilde yakalamıştır. Kısa vadeli tahminler, artış eğiliminin yakın gelecekte de devam edeceğini göstermektedir. Sonuç: Bu çalışma, Türkiye'de aşırı kilolu prevalansının gidişatının temelden anlaşılmasına katkıda bulunarak kanıta dayalı müdahaleler ve uzun dönemde sağlık planlaması için bir temel oluşturmaktadır.

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Cite This Article

Özen, H., Özen, D. (2025). Trends and Forecasts of Overweight Prevalence in Türkiye: A Time Series Approach Using ARIMA Models. Acta Medica Nicomedia, Cilt: 8 Sayı: 1, 52–58. https://doi.org/10.53446/actamednicomedia.1603153

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