Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.1-7
2025/12/22, Volume 13, Issue 5, pp.717-726

Diabetes Prevalence Forecasting Using GARCH and Deep Learning Models


Aida Abdulhssein Mohammed, Maytham Mohammad Bakr and Rdab Tark Abdalla


Abstract: The aim of this study is to use cutting-edge statistical and artificial intelligence techniques to assess and forecast the prevalence of type 1 diabetes in Iraqi children. In order to determine the prospective ultimate caseload and the fictitious inflection point, the research used the generalised autoregressive conditional variance (GARCH(p,q)) model to estimate the temporal behaviour of the data. The models under test were subjected to statistical stability conditions, and the AIC and BIC information criteria were used to assess their performance. The findings indicated that, out of all the nonparametric models examined, the GARCH(1,0) model performed the best. However, the paper showed that there were no significant variance fluctuations in the incidence data, which restricts the use of strictly nonparametric models. Deep LSTMNs neural networks were used for time series analysis in order to get around this. Using the ideal parameters for the number of layers and iterations, the data was divided into an 80% training set and a 20% test set. In comparison to other models, the LSTMNs findings showed the lowest mean squared errors, indicating a good capacity to predict future values. This implies that an efficient method for tracking upcoming changes in chronic illnesses is to combine deep learning algorithms with traditional statistical models. To enhance planning and response to the epidemic load of chronic illnesses, the paper suggests using this strategy in medical modelling and health forecasting.

Keywords: Type 1 Diabetes, GARCH, LSTM, Time Series, Forecasting, Statistical Stability.

DOI: Under indexing

Download: PDF

References:

  1. A. M. Nour ElDin Abd ElBaky, et al., “Role of epicardial fat thickness and irisin levels in early prediction of cardiac dysfunction in children and adolescents with type 1 diabetes mellitus,” Pediatria Polska - Polish Journal of Paediatrics, vol. 98, no. 4, pp. 278-284, 2023, [Online]. Available: https://doi.org/10.5114/polp.2023.133530.
  2. N. Lubasinski, et al., “Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review,” Nutrients, vol. 16, no. 14, p. 2214, Jul. 2024, [Online]. Available: https://doi.org/10.3390/nu16142214.
  3. K. Pang, “A comparative study of explainable machine learning models with Shapley values for diabetes prediction,” Healthcare Analytics, vol. 7, 2025, [Online]. Available: https://doi.org/10.1016/j.health.2025.100390.
  4. A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist System for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 5, pp. 855-868, May 2009, [Online]. Available: https://doi.org/10.1109/TPAMI.2008.137.
  5. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” J. Econometrics, vol. 31, no. 3, pp. 307-327, 1986, [Online]. Available: https://doi.org/10.1016/0304-4076(86)90063-1.
  6. B. Abuyassin and I. Laher, “Diabetes epidemic sweeping the Arab world,” World J. Diabetes, vol. 7, no. 8, pp. 165-174, 2016, [Online]. Available: https://doi.org/10.4239/wjd.v7.i8.165.
  7. R. F. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates variance of United Kingdom Inflation,” J. Econometrica, vol. 50, no. 4, pp. 987-1008, 1982.
  8. C. Francq and J. M. Zakoian, GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd ed., John Wiley & Sons, Ltd, 2019, pp. 1-14, [Online]. Available: https://doi.org/10.1002/9781119313472.ch1.
  9. L. R. Glosten, R. Jagannathan, and D. E. Runkle, “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” J. Finance, vol. 48, no. 5, pp. 1779-1801, 1993.
  10. D. B. Nelson, “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, vol. 59, no. 2, pp. 347-370, 1991, [Online]. Available: https://doi.org/10.2307/2938260.
  11. I. M. El-Kebbi, et al., “Epidemiology of type 2 diabetes in the Middle East and North Africa: Challenges and call for action,” World J. Diabetes, vol. 12, no. 9, pp. 1401-1425, 2021, [Online]. Available: https://doi.org/10.4239/wjd.v12.i9.1401.
  12. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, [Online]. Available: https://doi.org/10.1162/neco.1997.9.8.1735.
  13. J. M. Ahn, J. Kim, and K. Kim, “Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting,” Toxins, vol. 15, no. 10, 2023, [Online]. Available: https://doi.org/10.3390/toxins15100608.
  14. R. F. Engle III, “Risk and Volatility: Econometric Models and Financial Practice,” Nobel Lecture, pp. 326-349, Dec. 2003.
  15. A. A. Mohammad and N. S. K. Aljboori, “Employment of GARCH Model and L.S.M.E Method in Time Series with Application to COVID-19 Virus,” J. Algebraic Stat., vol. 13, no. 3, pp. 4856-4867, 2022, [Online]. Available: https://publishoa.com/index.php/journal/article/view/1330.
  16. E. M. Epaphra, “Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models,” J. Math. Finance, vol. 7, pp. 121-143, 2017, [Online]. Available: https://doi.org/10.4236/jmf.2017.71007.
  17. A. A. Mohammad and A. J. Salim, “The Analysis and Modeling of the time series of annual mean temperature in Mosul City,” Rafidain J. Sci., vol. 7, no. 1, pp. 37-48, 1996.
  18. S. Lee, C. K. Kim, and D. Kim, “Monitoring Volatility Change for Time Series Based on Support Vector Regression,” Entropy, vol. 22, no. 11, 2020, [Online]. Available: https://doi.org/10.3390/e22111312.
  19. S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting Methods and Applications, 3rd ed., John Wiley & Sons, Inc., 1998.
  20. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716-723, Dec. 1974, [Online]. Available: https://doi.org/10.1109/TAC.1974.1100705.
  21. J. Fan and Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods, Springer-Verlag New York, Inc., 2003.

    HOME

       - Conference
       - Journal
       - Paper Submission to Conference
       - Paper Submission to Journal
       - Fee Payment
       - For Authors
       - For Reviewers
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceeding


    PROCEEDINGS

       - Volume 13, Issue 5 (ICAIIT 2025)
       - Volume 13, Issue 4 (ICAIIT 2025)
       - Volume 13, Issue 3 (ICAIIT 2025)
       - Volume 13, Issue 2 (ICAIIT 2025)
       - Volume 13, Issue 1 (ICAIIT 2025)
       - Volume 12, Issue 2 (ICAIIT 2024)
       - Volume 12, Issue 1 (ICAIIT 2024)
       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    LAST CONFERENCE

       ICAIIT 2026
         - Photos
         - Reports

    PAST CONFERENCES

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.