A Mobile App to Predict Disease Based on Symptoms Using Artificial Intelligence

H’ng, Sheng Wei (2023) A Mobile App to Predict Disease Based on Symptoms Using Artificial Intelligence. [Final Year Project Report] (Unpublished)

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Abstract

As we knew nowadays, the world was surrounded by different types of diseases, and they caused humans to live in fear of disease and even brought death, especially of Coronavirus Disease (Covid�19). Therefore, it is important to develop a disease prediction to give early detection of diseases that might be infected by humans. The healthcare department has been doing much research in the fields of intelligent consultation, disease diagnosis, intelligent question-answering doctors like AI chatbot and so on. This had made many achievements. To improve medical technology, this study intends to use healthcare data analysis combined with machine learning knowledge to provide patients with a simple disease prediction which is usually neglected for lacking professional knowledge of the disease. This helps patients to get a suitable way of treatment in a short time before their health condition gets worse and worse. A different suitable machine learning algorithm will be used in the prediction system to predict the disease based on the symptoms of patients. To reduce time, the Chabot feature was used too so that patients were able to save time without meeting for a doctor to get treatment. The result at the end will show that our approach improved the accuracy of disease prediction based on symptoms with different evaluation metrics.

Item Type: Final Year Project Report
Additional Information: Project report (B.Sc.) -- Universiti Malaysia Sarawak, 2023.
Uncontrolled Keywords: Covid-19, Mobile App, Predict Disease Based on Symptoms, Artificial Intelligenc
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology
Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Unai
Date Deposited: 17 Jan 2024 02:14
Last Modified: 17 Jan 2024 02:14
URI: http://ir.unimas.my/id/eprint/44149

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