Modeling National Trends on Health in the Philippines Using ARIMA

TL;DR

This study applies ARIMA time series modeling to analyze and forecast health trends related to the leading causes of death in the Philippines, providing visualizations and future projections of disease patterns.

Contribution

It introduces the use of ARIMA models for trend analysis and forecasting of multiple health indicators in the Philippines, filling a gap in predictive health data analysis.

Findings

Certain diseases show increasing or stable trends in forecasts.

Some health conditions are projected to decrease in the future.

Forecasting reveals variable behaviors among different diseases.

Abstract

Health is a very important prerequisite in peoples well-being and happiness. Several studies were more focused on presenting the occurrence on specific disease like forecasting the number of dengue and malaria cases. This paper utilized the time series data for trend analysis and data forecasting using ARIMA model to visualize the trends of health data on the ten leading causes of deaths, leading cause of morbidity and leading cause of infants deaths particularly in the Philippines presented in a tabular data. Figures for each disease trend are presented individually with the use of the GRETL software. Forecasting results of the leading causes of death showed that Diseases of the heart, vascular system, accidents, Chronic lower respiratory diseases and Chronic Tuberculosis (all forms) showed a slight changed of the forecasted data, Malignant neoplasms showed unstable behavior of the forecasted data, and Pneumonia, diabetes mellitus, Nephritis, nephrotic syndrome and nephrosis and certain conditions originating in perinatal showed a decreasing patterns based on the forecasted data.