Development of an intelligent expert system with deep learning for early risk detection of monkeypox
Keywords:
Monkeypox, Expert System, Deep Learning, CNN, AndroidAbstract
This study aims to develop an intelligent expert system based on deep learning to detect the risk of Monkeypox disease early. Monkeypox is a zoonotic disease that can spread to humans, with symptoms similar to smallpox but milder. Early detection is essential to prevent spread and speed up handling. In this system, deep learning models are used to analyze data on clinical symptoms and patient risk factors, including travel history, interactions with animals, and physical symptoms that appear. The expert system provides a prediction of the level of risk and recommendations for medical treatment based on the results of the analysis. The system was tested using a dataset of 770 images divided into four classes (Chickenpox, Measles, Monkeypox, and Normal), with 70% for training, 20% for testing, and 10% for validation. The evaluation results showed an accuracy of 96% in detecting Monkeypox risk, making it an effective tool for medical personnel in diagnosing and providing early treatment. The implementation of this expert system is expected to contribute to preventing the spread of Monkeypox disease more efficiently and in a timely manner.
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Copyright (c) 2025 Berliana Rahmadhani, Adam Fathurrohman Arya Bakhti, Rian Ardianto, Anggit Wirasto, Khoirun Nisa (Author)
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