Machine Learning-Based Prediction of Seasonal Influenza Trends in Saudi Arabia: A Tool for Regional Public Health Planning

Alshaikh A.  Shokeralla; Fathelrhman  El Guma; Ali H.  Abdalla; Amal E.Y.  Hagsddig; Rahma AbuBakr  Musa; M.A.M.  Eltaweel; Abdelaziz H.  Elawad; Ibrahim  Elshamy

doi:10.6000/1929-6029.2025.14.64

Authors

Alshaikh A. Shokeralla Department of Mathematics, Faculty of Science, Al-Baha University, Al-Aqiq 65931, Saudi Arabia
Fathelrhman El Guma Department of Mathematics, Faculty of Science, Al-Baha University, Al-Aqiq 65931, Saudi Arabia
Ali H. Abdalla Department of Mathematics, Faculty of Science, Al-Baha University, Al-Aqiq 65931, Saudi Arabia
Amal E.Y. Hagsddig Department of Statistics, Faculty of Engineering, Mashreq University, Khartoum, Sudan
Rahma AbuBakr Musa Biology Department, Faculty of Science, Al-Baha University, Saudi Arabia and Biology Department, Faculty of Applied and Industrial Science, Bahri University, Sudan
M.A.M. Eltaweel Department of Mathematics, Faculty of Science, Al-Baha University, Al-Aqiq 65931, Saudi Arabia and Faculty of Science Ain Shams University Cairo Egypt
Abdelaziz H. Elawad Department of Mathematics, Faculty of Science, Al-Baha University, Al-Aqiq 65931, Saudi Arabia
Ibrahim Elshamy Higher Institute of Engineering & Technology, Almanzala, Egypt and Mathematics Department, Faculty of Science, Al Baha University, KSA

DOI:

https://doi.org/10.6000/1929-6029.2025.14.64

Keywords:

Forecasting, Influenza, Machine learning, Random Forest, Saudi Arabia, Support Vector Regression

Abstract

Influenza has continued to be a worldwide social health problem, especially in high-density population areas with minimal early-warning mechanisms. The present study evaluates the predictive ability of two machine learning models Support Vector Regression (SVR) and Random Forest (RF) to predict weekly influenza cases in Saudi Arabia, spanning from 2017 to 2022, on 313 weekly influenza records in the WHO Global Influenza Surveillance and Response System (GISRS). The performance of these models was measured with R², MAE, MSE, and RMSE. Although SVR had a better training accuracy (R² = 0.96), RF had a better generalization (R² = 0.818) and more consistent predictions at the peaks of the seasons. These observations show that RF is appropriate to real-time influenza surveillance and can provide a replicable and versatile framework to assist data-driven epidemic preparedness in Saudi Arabia and other similar contexts throughout MENA and Asia-Pacific.

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