Performance Analysis of Machine Learning Classification Approaches for Monkey Pox Disease Prediction

Abstract

After covid-19 pandemic, many countries have the possibility of getting affected by Monkey Pox Virus. Monkey pox has the same symptoms of smallpox, chicken pox, and measles virus. In this work, the computational models are construed to predict the presence or absence of monkey pox virus. Eight different Classification algorithms including Decision Tree (DT), Random Forest Classification (RF), Naïve Bayes (NB), K-Nearest Neighbor algorithms (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boosting algorithm (AB), Gradient Boosting (GB) algorithm are used for the Classification of Monkey Pox disease. Four evaluation measures are used in this work to compute the accuracy of classification. Four measures F-Score, Accuracy, Precision, and Recall are used to compare the eight different types of classification algorithms. Based on experimental analysis, it was observed that highest accuracy of 71% is achieved by Gradient Boosting algorithm when compared to other algorithms.