The re-surfacing of Monkeypox (Mpox) a zoonotic viral disease that causes painful blisters on the skin of the infected, as a global health concern has triggered the rapid and important development of useful and effective diagnostic tools to bolster the existing traditional methods such as contact tracing and timely intervention. Machine learning (ML) algorithms have shown promising results in the early detection and diagnosis of infectious diseases, including Mpox, by leveraging diverse data sources such as electronic health records, clinical images, and laboratory results. This Systematic Literature Review aims to assess the effectiveness of machine learning algorithms in detecting Monkeypox (Mpox). The main question is: ‘How well do machine learning algorithms perform in detecting Monkey Pox?”The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were employed for this review. An in-depth search was done spanning several databases, namely ScienceDirect, IEEE Xplore, ACM Digital Library, and Springer Link. Studies published between 2022 and 2024 were considered. After removing duplicates and screening for relevance and quality, a total of 30 papers were included in the final analysis. The outcomes indicate that ML algorithms, particularly convolutional neural networks (CNNs) and support vector machines (SVMs), have proven to have high accuracy and sensitivity in detecting Mpox. These algorithms perform strongly compared to traditional diagnostic methods like contact tracing, resulting in faster and more reliable results. The integration of ML with imaging techniques, such as dermoscopy and radiography, has further enhanced diagnostic precision. (ML)algorithms present a notable advancement in the detection of Monkeypox. They are very robust in the face of large datasets and give a very high level of accuracy and precision in interpreting the datasets, to this effect however further research is needed to address the challenges of data variability and the need for large, high-quality datasets. The review's findings offer practitioners and policymakers in healthcare important new information. The creation of novel diagnostic procedures and guidelines can be influenced by the proven efficacy of machine learning algorithms in Mpox detection. The results also emphasize the necessity of funding machine learning research and infrastructure to enhance disease surveillance and response tactics.

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Mrs. Maybin Dzikiti