Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches

Halil Çolak

doi:10.31466/kfbd.1713450

TR EN

1975'ten 2016'ya Çin'de Yetişkin Obezitesinin Modellenmesi: Büyüme Eğrileri ve Zaman Serisi Yaklaşımlarının Karşılaştırmalı Analizi

Abstract

Bu araştırma, 1975'ten 2016'ya kadar Çin'deki yetişkin obezite yaygınlığını tahmin etmek için dört tahmin modelini değerlendirmektedir: Gompertz, Lojistik, Otoregresif Entegre Hareketli Ortalama (ARIMA) ve Uzun Kısa Süreli Bellek (LSTM). Model performansı, Dünya Sağlık Örgütü verilerine dayanan R², MSE, RMSE ve SSE ölçümleri kullanılarak değerlendirilmiştir. Sonuçlar, Çin'in hızlı kentleşmesini, ekonomik gelişimini ve diyet değişikliklerini yansıtan obezitede sürekli ve doğrusal olmayan bir artış olduğunu göstermiştir. Lojistik model, obezitenin sigmoidal ilerlemesini doğru bir şekilde tasvir ederek maksimum doğruluğa ulaşmıştır (R² = 0,9997; RMSE = 0,0312). Gompertz modeli (R² = 0,9961) asimetrik uzun vadeli büyümeyi etkili bir şekilde temsil etmiş, ARIMA modeli (R² = 0,8098) ise kısa vadeli tahminde mükemmel olmuş ancak doğrusal olmayan dinamikleri yetersiz bir şekilde tasvir etmiştir. LSTM modeli (R² = 0,8199; RMSE = 0,0582), beş katlı çapraz doğrulama ile doğrulandığı üzere, güçlü bir uyum ve zamansal öğrenme sergilemiştir. Araştırmalar, Çin'deki obezitenin henüz doygunluğa ulaşmadığını ve acil ve kanıta dayalı sağlık tedavilerine olan ihtiyacı vurgulamaktadır. Yorumlanabilir büyüme modellerinin uyarlanabilir derin öğrenme mimarileriyle birleştirilmesi, kronik hastalık eğilimlerini tahmin etmek ve kanıta dayalı halk sağlığı müdahalelerini kolaylaştırmak için güçlü bir hibrit çerçeve oluşturmaktadır.

Keywords

Yetişkin obezitesi, Büyüme eğrileri, Zaman serisi, Tahmin, Çin

Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches

Abstract

This research evaluates four forecasting models—Gompertz, Logistic, Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM)—to project adult obesity prevalence in China from 1975 to 2016. Model performance was assessed using R², MSE, RMSE, and SSE measures based on World Health Organisation data. The results indicated a continuous and nonlinear increase in obesity, mirroring China's swift urbanisation, economic development, and dietary changes. The Logistic model attained the maximum accuracy (R² = 0.9997; RMSE = 0.0312), accurately depicting the sigmoidal progression of obesity. The Gompertz model (R² = 0.9961) effectively represented asymmetric long-term growth, whereas the ARIMA model (R² = 0.8098) excelled in short-term forecasting but inadequately depicted nonlinear dynamics. The LSTM model (R² = 0.8199; RMSE = 0.0582) exhibited robust adaptability and temporal learning, as validated by five-fold cross-validation. Research indicates that obesity in China has not yet attained saturation, highlighting the necessity for immediate and evidence-based health treatments. The amalgamation of interpretable growth models with adaptive deep-learning architectures establishes a formidable hybrid framework for predicting chronic disease trends and facilitating evidence-based public health interventions.

Keywords

Adult obesity, Growth curves, Time series, Forecasting, China

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English

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Software Engineering (Other)

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Research Article

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Halil Çolak ^*
0000-0001-9003-106X
Türkiye

Publication Date

March 10, 2026

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June 3, 2025

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December 4, 2025

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Year 2026 Volume: 16 Number: 1

DOI

https://doi.org/10.31466/kfbd.1713450

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https://izlik.org/JA73KL25HL

APA

Çolak, H. (2026). Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches. Karadeniz Fen Bilimleri Dergisi, 16(1), 475-496. https://doi.org/10.31466/kfbd.1713450

AMA

1.Çolak H. Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches. KFBD. 2026;16(1):475-496. doi:10.31466/kfbd.1713450

Chicago

Çolak, Halil. 2026. “Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches”. Karadeniz Fen Bilimleri Dergisi 16 (1): 475-96. https://doi.org/10.31466/kfbd.1713450.

EndNote

Çolak H (March 1, 2026) Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches. Karadeniz Fen Bilimleri Dergisi 16 1 475–496.

IEEE

[1]H. Çolak, “Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches”, KFBD, vol. 16, no. 1, pp. 475–496, Mar. 2026, doi: 10.31466/kfbd.1713450.

ISNAD

Çolak, Halil. “Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches”. Karadeniz Fen Bilimleri Dergisi 16/1 (March 1, 2026): 475-496. https://doi.org/10.31466/kfbd.1713450.

JAMA

1.Çolak H. Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches. KFBD. 2026;16:475–496.

MLA

Çolak, Halil. “Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches”. Karadeniz Fen Bilimleri Dergisi, vol. 16, no. 1, Mar. 2026, pp. 475-96, doi:10.31466/kfbd.1713450.

Vancouver

1.Halil Çolak. Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches. KFBD. 2026 Mar. 1;16(1):475-96. doi:10.31466/kfbd.1713450

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1975'ten 2016'ya Çin'de Yetişkin Obezitesinin Modellenmesi: Büyüme Eğrileri ve Zaman Serisi Yaklaşımlarının Karşılaştırmalı Analizi

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Modelling Adult Obesity in China From 1975 to 2016: A Comparative Analysis of Growth Curves and Time Series Approaches

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