Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan

Abstract

Today, artificial intelligence is a key tool for turning a city into a smart city, and advances in information and communication technology (ICT) have led to the development of smart cities with many different parts. Smart Health is one of these components and is used to improve healthcare by providing services such as disease forecasting, early diagnosis, and others. There are various machine learning algorithms available now that can help with S-Health services, but which is better for disease forecasting? Gedaref State, for example, has some of Sudan's heaviest rains, and malaria and pneumonia are widespread throughout the year. Predicting future trends for these diseases has been a major focus for researchers in order for Gedaref's administration and the state's ministry of health to design effective ways to prevent and control the development of these diseases, as well as to prepare an adequate stock of medicine. As a result, it is necessary to establish a trustworthy and accurate forecasting model to aid Gedaref's government in developing economic and medical strategies for dealing with these diseases, as well as taking action on medical resource allocation. This study uses a time series dataset collected from the state's ministry of health to estimate malaria and pneumonia as common diseases in Gedaref state, Sudan, five months later. To comprehend the overall number of cases of diseases, two forecasting methodologies, namely the ARIMA and Prophet models, are applied to the disease's dataset. The performance of the ARIMA and FB-Prophet forecasting systems in predicting malaria and pneumonia diseases in Gedaref State is compared in this study. The data was collected from the state's ministry of health between January 2017 and December 2021. The results reveal that the ARIMA technique outperforms the FB-Prophet forecasting method in both malaria (RMSE: 182.8, MAE: 141.6, MAPE: 0.0057, and MASE: 0.0537) and pneumonia (RMSE: 1400.3, MAE: 1001.4, MAPE: 0.0513, and MASE: 0.9136).