Acta Scientific Computer Sciences

Research Article Volume 5 Issue 4

Prevalence of MPX (Monkeypox) by Using Machine Learning Approaches

V Kakulapati*, Chinta Venkata Naveen, Gangishetti Abhiram and V Aneesh

Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India

*Corresponding Author: V Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.

Received: March 23, 2023; Published: April 06, 2023

Abstract

There are many similarities between the symptoms of monkeypox and the symptoms of smallpox, although the clinical severity of monkeypox is less severe than that of smallpox. Since the 1980s, when smallpox was declared eradicated, and smallpox vaccinations were discontinued, monkeypox has become the most significant orthopox virus in public health. Monkeypox is mainly found in central and west Africa, becoming more prevalent in urban areas. Non-human primates and rodents are examples of animal hosts. After smallpox was eradicated in the Democratic Republic of Congo in 1968, a 9-month-old baby was diagnosed with monkeypox in 1970. This virus is now spreading across Africa's central and western rainforest areas, with most cases occurring in the Democratic Republic of the Congo and its neighboring countries. Since the spread of monkeypox and other deadly illnesses is on everyone's mind, the development of suitable modeling methodologies and methods is being considered. In India, the occurrence has reached epidemic levels, and this illness is becoming more common. Polynomial regression analysis approaches were used to estimate the number of monkeypox patients. To model and forecast monkey pox occurrences, we used a combination of decision trees, polynomials, and random forests to classify the illness and estimate its prevalence with high confidence.

Keywords: Linear Regression; Decision; Prevalence; Model; COVID-19; Classification; Pox; Confidence

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Citation

Citation: V Kakulapati., et al. “Prevalence of MPX (Monkeypox) by Using Machine Learning Approaches". Acta Scientific Computer Sciences 5.5 (2023): 10-15.

Copyright

Copyright: © 2023 V Kakulapati., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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