Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm

Abstract

Monkeypox is a zoonotic viral disease that the World Health Organization (WHO) reported as an epidemic in 2022. In most nations, the rate of these illness infections started to rise over time. Monkeypox can be caught directly from an infected person or via animal contact. In this study, an artificial intelligence-based diagnostic model for early monkeypox infection detection is developed. The proposed method is based on building a model based on KNN, SVC, Random Forest, Naive Bayes and Gradient Boosting for the classification problem. A voting method was also used to determine the final diagnosis of the proposed model. The system was trained and evaluated using a dataset that represented the clinical signs of monkeypox infection. The dataset comprises one hundred twenty infected patients and 120 typical cases out of 240 probable cases. The suggested model attained 75% accuracy.

Keywords

• Monkeypox
• Monkeypox Clinical Symptoms
• Classification
• Machine Learning

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