Onyeacholem Ifeanyi Joshua

Work place: ICT Directorate, Delta State University, Abraka, Nigeria

E-mail: ifeanyionyeacholem@yahoo.co.uk


Research Interests: Artificial Intelligence, Machine Learning


Onyeacholem Ifeanyi Joshua just concluded is Master’s Degree program with the Computer Science Department, Delta State University Abraka, with research interest in Machine learning and artificial intelligence.

Author Articles
Monkey Pux Data: Visualization and Prediction of the Observed Number of Affected People in Nigeria

By Okorodudu Franklin Ovuolelolo Onyeacholem Ifeanyi Joshua Gracious C. Omede

DOI: https://doi.org/10.5815/ijeme.2024.03.04, Pub. Date: 8 Jun. 2024

Information and communication technology (ICT) is the bedrock of information dissemination and a driving force for better economic planning to achieve its goals and get success stored securely and confidentially. Monkeypox (MPX) epidemic outbreaks affect human beings as a whole and can be a cause of serious illness and death. This epidemic continues to challenge medical systems worldwide in many aspects, including sharp increases in demands for hospital beds and critical shortages in medical equipment, while many healthcare workers have themselves been infected. Thus, the capacity for immediate clinical decisions and effective usage of healthcare resources is crucial. Therefore, this research has developed an effective screening system that will enable quick and efficient diagnosis of Monkeypox (MPX) and can mitigate the burden on healthcare systems. This system would be handy in sharing much-needed expert knowledge in the diagnosis of Monkeypox (MPX) symptoms since it would be used by medical officers, clinical officers, and nurses in the absence of specialists. It could be used to collect medical data, which in this case is the symptoms presented by the patients; it can also be useful in training general practitioners, physicians, inexperienced nurses, and paramedics to guarantee suitable and accurate decision-making in the diagnosis and management of Monkeypox (MPX). The methodology adopted is the machine learning algorithms foranalysis and training of our dataset, to ascertain the level at which this epidemic has caused harm to lives, a linear relationship between an independent and dependent variable is provided by the linear regression technique, and Python programming was used to visualize and predict clinical outcomes.

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