Open Access

ARTICLE

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

by Liaqat Ali¹, Saif E. A. Alnawayseh², Mohammed Salahat³, Taher M. Ghazal^4,5,*, Mohsen A. A. Tomh⁶, Beenu Mago⁷

1 College of Engineering and Technology, University of Science and Technology of Fujairah, Fujairah, UAE
2 Electrical Engineering Department, Faculty of Engineering, Mutah University, Jordan
3 College of Engineering and Technology, University of Science and Technology of Fujairah, Fujairah, UAE
4 School of Information Technology, Skyline University College, University City Sharjah, 1797, UAE
5 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Malaysia
6 Faculty of Computer Science, NCBA&E, Lahore, 54660, Pakistan
7 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE

* Corresponding Author: Taher M. Ghazal. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 1095-1104. https://doi.org/10.32604/iasc.2023.031335

Received 15 April 2022; Accepted 06 July 2022; Issue published 29 September 2022

Abstract

The immediate international spread of severe acute respiratory syndrome revealed the potential threat of infectious diseases in a closely integrated and interdependent world. When an outbreak occurs, each country must have a well-coordinated and preventative plan to address the situation. Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living. Although intelligent devices and applications have become a vital part of our everyday lives, smart gadgets have also led to several physical and psychological health problems in modern society. Here, we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network (ANN). The ANN improved the regularization of the classification model, hence increasing its accuracy. The unconstrained optimization model reduced the classifier’s cost function to obtain the lowest possible cost. To verify the performance of the intelligent system, we compared the outcomes of the suggested scheme with the results of previously proposed models. The proposed intelligent system achieved an accuracy of 0.89, and the miss rate 0.11 was higher than in previously proposed models.

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Keywords

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