Forecasting Chronic Kidney Disease Mortality in Cambodia Using Time Series Analysis

Main Article Content

Dr.G. Mokesh Rayalu

Abstract

An alarming rise in the number of fatal instances of chronic kidney disease (CKD) has made it a top public health priority in Cambodia. Future trends in CKD-related fatalities in Cambodia are projected using the Autoregressive Integrated Moving Average (ARIMA) model, which is the focus of this research. The Box-Jenkins procedure, the Autocorrelation Function (ACF), the Partial Autocorrelation Function (PACF), and the Augmented Dickey-Fuller (ADF) test were all used to guarantee the model's integrity. These investigations helped clarify whether or not the time series data were stationary and guided the selection of reasonable ARIMA model parameters. Incorporating these approaches led to the creation of a robust forecasting model that sheds light on the likely course of CKD-related mortality in Cambodia. The findings of this study aid in the creation of efficient preventative measures and focused therapies to lessen the national burden of CKD.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Rayalu, D. M. . (2019). Forecasting Chronic Kidney Disease Mortality in Cambodia Using Time Series Analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(1), 657–665. https://doi.org/10.61841/turcomat.v10i1.14251
Section
Research Articles

References

Bujang, M. A., Adnan, T. H., Hashim, N. H., Mohan, K., Kim Liong, A., Ahmad, G., ... & Haniff, J.

(2017). Forecasting the incidence and prevalence of patients with end-stage renal disease in

Malaysia up to the year 2040. International journal of nephrology, 2017.

He, Z., & Tao, H. (2018). Epidemiology and ARIMA model of positive-rate of influenza viruses

among children in Wuhan, China: A nine-year retrospective study. International Journal of

Infectious Diseases, 74, 61-70.

Ahmad, W. M. A. W., Mohd Noor, N. F., Mat Yudin, Z. B., Aleng, N. A., & Halim, N. A. (2018).

TIME SERIES MODELING AND FORECASTING OF DENGUE DEATH OCCURRENCE IN

MALAYSIA USING SEASONAL ARIMA TECHNIQUES. International Journal of Public Health

& Clinical Sciences (IJPHCS), 5(1).

Terner, Z., Carroll, T., & Brown, D. E. (2014, October). Time series forecasts and volatility

measures as predictors of post-surgical death and kidney injury. In 2014 IEEE Healthcare

Innovation Conference (HIC) (pp. 319-322). IEEE.

Villani, M., Earnest, A., Nanayakkara, N., Smith, K., De Courten, B., & Zoungas, S. (2017). Time

series modelling to forecast prehospital EMS demand for diabetic emergencies. BMC health

services research, 17, 1-9.

Yang, J., Li, L., Shi, Y., & Xie, X. (2018). An ARIMA model with adaptive orders for predicting

blood glucose concentrations and hypoglycemia. IEEE journal of biomedical and health

informatics, 23(3), 1251-1260.

Singye, T., & Unhapipat, S. (2018, June). Time series analysis of diabetes patients: A case study of

Jigme Dorji Wangchuk National Referral Hospital in Bhutan. In Journal of Physics: Conference

Series (Vol. 1039, No. 1, p. 012033). IOP Publishing.

Rodríguez-Rodríguez, I., Rodríguez, J. V., Chatzigiannakis, I., & Zamora Izquierdo, M. A. (2019).

On the possibility of predicting glycaemia ‘on the fly’with constrained IoT devices in type 1

diabetes mellitus patients. Sensors, 19(20), 4538.

Pan, Y., Zhang, M., Chen, Z., Zhou, M., & Zhang, Z. (2016, June). An ARIMA based model for

forecasting the patient number of epidemic disease. In 2016 13th International Conference on

Service Systems and Service Management (ICSSSM) (pp. 1-4). IEEE.

Velasco, J. M., Garnica, O., Contador, S., Botella, M., Lanchares, J., & Hidalgo, J. I. (2017, July).

Forecasting glucose levels in patients with diabetes mellitus using semantic grammatical evolution

and symbolic aggregate approximation. In Proceedings of the Genetic and Evolutionary

Computation Conference Companion (pp. 1387-1394).

Bunescu, R., Struble, N., Marling, C., Shubrook, J., & Schwartz, F. (2013, December). Blood

glucose level prediction using physiological models and support vector regression. In 2013 12th

International Conference on Machine Learning and Applications (Vol. 1, pp. 135-140). IEEE.

Plis, K., Bunescu, R., Marling, C., Shubrook, J., & Schwartz, F. (2014, June). A machine learning

approach to predicting blood glucose levels for diabetes management. In Workshops at the TwentyEighth AAAI conference on artificial intelligence.