FORECASTING THE NUMBER OF HYPERTENSIVE PATIENTS IN SAM KHOK DISTRICT, PATHUM THANI PROVINCE
Keywords:
Hypertension, Forecasting, Number of patients, Sam Khok District, Grey system theory, Box-Jenkins methodAbstract
This study aimed to forecast the number of hypertensive patients in Sam Khok district, Pathum Thani province. It was the predictive research using time series methods. The data on the number of hypertensive patients in Sam Khok district, Pathum Thani province between fiscal years 2013 and 2024, collected from the reporting system of the Ministry of Public Health. Data were analyzed using the models in polynomial regression, the Grey system theory, and Box-Jenkins method.
The results showed that when using the number of hypertensive patients in Sam Khok District, Pathum Thani Province, fiscal years 2013 to 2023, all models had a mean absolute percentage error (MAPE) less than 10, which can be used to predict with high accuracy. The GM(1,1)EPC model had the lowest MAPE value of 2.55 and the highest coefficient of determination of 99.21, which had the highest accuracy during the model development period. However, the number of patients in the fiscal year 2024, which was compiled on September 15, 2024, was 9,307. If the data is still being reported into the system, it is estimated that they were approximately 4.10 percent of patients that will be entered into the system until September 30, 2024. The ARIMA (0, 1, 0) with drift model forecasted the number of hypertensive patients in Sam Khok District in fiscal year 2024 will be 9,673 cases, an increase from the fiscal year 2023 of 3.93 percent, which was the appropriate forecast value and will be used in public health administration regarding the prevalence of hypertension.
Downloads
References
Thai Hypertension Society. Hypertension. [online] 2024. [cited 2024 Sep 2]. Available from: http://www.thaihypertension.org/hypertensiondetail.php?n_id=338. [in Thai].
World Health Organization (WHO). Hypertension [online] 2023 [cited 2024 Sep 2]. Available from: https://www.who.int/news-room/fact-sheets/detail/hypertension
Ministry of Public Health. Standard Reporting Group >> Illness with important non-communicable diseases >> Rate of high blood pressure disease per population, processing date 20 September 2024. [online] 2024 [cited 2024 Sep 2]. Available from: https://hdcservice.moph.go.th/hdc/reports/report.php?&cat_id=6a1fdf282fd28180eed7d1cfe0155e11&id=6b9af46d0cc1830d3bd34589c1081c68. [in Thai].
Cubillas JJ, Ramos MI, Feito FR. Use of Data Mining to Predict the Influx of Patients to Primary Healthcare Centres and Construction of an Expert System. Appl. Sci. 2022; 12(22):11453. https://doi.org/10.3390/app122211453
Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice. 3rd edition. Melbourne, Australia: OTexts; 2021.
Ceylan Z. Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization. Applied Soft Computing 2021; 109: 107592.doi: 10.1016/j.asoc.2021.
Asante DO, Walker AN, Seidu TA, Kpogo SA, Zou J. Hypertension and Diabetes in Akatsi South District, Ghana: Modeling and Forecasting. Biomed Res Int 2022; 9690964: 1-12. doi: 10.1155/2022/9690964=
Suleman N, Sapong S. Statistical modeling of hypertension cases in Navrongo, Ghana, West Africa. Am. Int. J. Soc. Sci. 2011; 2(4): 377-383.
Mahidol University. Announcement of Mahidol University regarding guidelines for research projects that do not qualify as human research, 2022 [online] 2023 [cited 2024 Sep 2]. Available from: https://sp.mahidol.ac.th/th/LAW/policy/2565-MU-Non-Human.pdf
Central Institutional Review Board (MU-CIRB), Mahidol University. Self-Assessment form whether an activity is human subject research which requires ethical approval [online] 2022 [cited 2024 Sep 2]. Available from: https://sp.mahidol.ac.th/th/ethics-human/forms/checklist/2022-Human%20Research%20Checklist-researcher.pdf
Xie N. A summary of grey forecasting models. GREY SYST 2022; 12(4): 703–722. doi:10.1108/GS-06-2022-0066
Liu S. Grey system theory and its application. 9th ed. Beijing: Science Press; 2021.
Lin YH, Chiu CC, Lin YJ, Lee PC. Rainfall prediction using innovative grey model with the dynamic index. J Mar Sci Technol 2013; 21(1): 63-75. DOI:10.6119/JMST-011-1116-1.
Liu S, Lin Y. Grey system theory and its application. Berlin, Heidelberg: Springer; 2010.
R Core Team. R: A Language and environment for statistical computing. (Version 4.1) [online] 2021 [cited 2024 Sep 2]. Available from: https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01)
Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, et al. forecast: Forecasting functions for time series and linear models. R package version 8.23.0. [online] 2024 [cited 2024 Sep 2]. Available from: https://pkg.robjhyndman.com/forecast/.
Hyndman RJ, Khandakar Y. Automatic time series forecasting: The forecast package for R. J Stat Softw 2008; 27(3): 1-22.
Lewis CD. Industrial and business forecasting methods. London: Butterworths; 1982.
The Bureau of Registration Administration, Department of Provincial Administration. Population by age Separated by the population whose name is in the house registration, Sam Khok District, Pathumthani Province, data for June 2024 [online] 2024 [cited 2024 Sep 2]. Available from: https://stat.bora.dopa.go.th/stat/statnew/statMONTH/statmonth/#/view. [in Thai].
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles in this journal are copyrighted by the x may be read and used for academic purposes, such as teaching, research, or citation, with proper credit given to the author and the journal.use or modification of the articles is prohibited without permission.
statements expressed in the articles are solely the opinions of the authors.
authors are fully responsible for the content and accuracy of their articles.
other reuse or republication requires permission from the journal."