Chronic Kidney Disease Deaths in South Korea: Projecting Future Trends Appraise Time Series Analysis using Prediction Models

Abstract

The ARIMA model is utilized for the purposes of forecasting in this study, which delves into the
analysis of mortality caused by Chronic Kidney Disease (CKD) in South Korea. In order to investigate
the underlying patterns in the CKD mortality data, a number of diagnostic tests, including as the
Augmented Dickey-Fuller test, the autocorrelation function (ACF), the partial autocorrelation function
(PACF), and the Box-Jenkins model, were carried out. After that, the ARIMA model was used to
produce projections for the future based on the patterns seen in the past. These findings shed light on
probable patterns and oscillations in CKD death rates in South Korea, which offers critical insights for
public health interventions and policies that aim to reduce the burden of CKD in the country.