A Comprehensive Review on Optimizing Machine Learning Models for Early Detection and Forecasting of Monkeypox Outbreaks

Ahmed El-Sayed Saqr¹, Ahmed M. Elshewey², Sekar Kidambi Raju³, Marwa M. Eid⁴

¹Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Egypt

²Department of Computer Science, Faculty of Computers and Information, Suez University, Suez 43533, Egypt

³School of Computing, SASTRA Deemed University, Thanjavur 613401, India

⁴ Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt

Emails: a7mdsqr@std.mans.edu.eg; Ahmed.Elshewey@fci.suezuni.edu.eg; sekar1971kr@gmail.com; mmm@ieee.org

Abstract

This is a significant problem in diagnosing zoonotic opportunistic 'emerging' diseases like Monkeypox, which require not only better diagnostics but also efficient, effective, and affordable diagnostics. This paper considers the possibilities of machine learning (ML), deep learning (DL), and optimization algorithms for diagnosing and predicting Monkeypox. The presently employed strategies can be enhanced because clinical and imaging data can be harnessed to drive these technologies for early detection and subsequent containment activities. Generally, in a review, the authors offer information on how the diagnostic processes using ML and DL result in enhanced accuracy, specificity, and sensitivity of models, thus reducing design reliabilities. Furthermore, outbreak data is subjected to predictive modeling analysis to establish patterns useful in helping risk managers and policymakers prepare to manage future outbreaks. This system poses a new diagnostic model for Monkeypox and other zoonotic diseases by incorporating these complex computational tools into the present healthcare systems. This advancement not only strengthens the diagnostic arsenal of zoonotic diseases but also expands the possibilities for the interception and prevention of such diseases in the future at the world level.

Keywords: Artificial Intelligence (AI); Machine Learning (ML); Deep Learning (DL); Zoonotic Diseases; Transfer Learning; Metaheuristic Optimization