Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application
| Main Authors: | Salsabila, Dewi Salma, Tanamal, Rinabi |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Dr. Soetomo
, 2020
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| Subjects: | |
| Online Access: |
https://ejournal.unitomo.ac.id/index.php/inform/article/view/2771 https://ejournal.unitomo.ac.id/index.php/inform/article/view/2771/pdf https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2771/563 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2771/564 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2771/565 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2771/566 https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/2771/593 |
Daftar Isi:
- Shown symptoms in digestive diseases might be similar, resulting in patient’s suspected diseases before and after diagnosis attempt might turn out to be different. This paper aims to build a design of an expert system for digestive disease identification using Naïve Bayes methodology for iOS-based applications. The result from this paper helps medical interns to increase the accuracy in predicting patient’s suspected digestive disease. A precise prediction in suspected disease identification can minimalize unnecessary diagnosis attempts, which saves time and reduces cost. Naïve Bayes is chosen because it has a higher accuracy level than other classification methods. This research includes collecting data through literature reviews on digestive diseases and their symptoms, processing the data to be turned into a knowledge base for the expert system, conducting data training using Naïve Bayes by the designed expert system application through this research. The result from the conducted data training using Naïve Bayes methodology shows that the expert system application has a higher accuracy level, which is 84%.