APPLICATION OF CLASSIFICATION BASED ASSOCIATION (CBA) FOR MONKEYPOX DISEASE DETECTION
Abstract
Monkeypox is a zoonotic disease that can be transmitted from animals to humans. The monkeypox virus is the cause of monkeypox disease, which belongs to the orthopoxvirus family. Although the mortality rate from monkeypox is not as high as COVID-19, this virus can be the cause of the next global pandemic if the epidemic worsens. Therefore, it is very important to carry out proper surveillance and prevention to prevent the spread of this disease. In this study, researchers developed another method to detect monkeypox disease based on its symptoms using the classification by association (CBA) method. CBA integrates the advantages of classification and association analysis, allowing the classification process and a deeper understanding of the strength of the relationship between features in the dataset through the analysis of metrics such as support and confidence. Based on the results of the experiments in this study, an accuracy of 68.64%, a precision of 92.21%, and a sensitivity of 71.09% were obtained. In this case, the accuracy obtained is still low, but the results of other metrics show that the CBA model performs fairly well in predicting the positive class with high precision.
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