Comparison of Conventional Machine Learning and Deep Neural Network Algorithms in the Prediction of Monkey-Pox

Cucu Ika Agustyaningrum; Rizka Dahlia; Omar Pahlevi

doi:10.34288/jri.v5i3.217

Authors

Cucu Ika Agustyaningrum Universitas Bina Sarana Informatika
Rizka Dahlia Universitas Bina Sarana Informatika
Omar Pahlevi Universitas Bina Sarana Informatika

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i3.217

Keywords:

Conventional Machine Learning, Data Mining, Deep Neural Network, Monkeypox

Abstract

Smallpox syndrome, or monkeypox, is an uncommon zoonotic viral infection brought on by the monkeypox virus, which belongs to the genus Orthopoxvirus and family Poxviridae. Injury-related mortality in primates ranges from 1 to 10%. Data mining is a method for analyzing data. Deep neural networks and traditional machine learning methods are used in data analysis. The Python programming language is used during the comparison procedure of this research algorithm to generate values for accuracy, f1 score, precision, recall, ROC, and AUC. The test results demonstrate that using sigmoid activation function parameters, the deep neural network algorithm's accuracy is 70.08%, F1 score is 79.18%, precision is 68.59%, recall is 62.65%, and AUC is 62.65%. Compared to conventional machine learning algorithms, the adagrad optimizer has a higher value with a learning rate of 0.01 and 0.2 dropouts. The conventional machine learning model algorithm has the best xgboost, F1 score, precision, recall, and AUC scores compared to other approaches: 64.40%, 64.45%, and 78.14%. According to these numbers, the average fairness disparity between deep neural network algorithms and traditional machine learning is 5.68%, F1 score is 13.79%, precision is 4.14%, recall is 1.75%, and AUC is 1.75%.

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