Forecasting Chronic Kidney Disease Mortality in Cambodia Using Time Series Analysis
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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.
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