Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application

  • Dewi Salma Salsabila Universitas Ciputra
  • Rinabi Tanamal Universitas Ciputra
Keywords: digestive disease, expert system, naïve Bayes, iOS, knowledge-based


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%.

Author Biographies

Dewi Salma Salsabila, Universitas Ciputra
Department of Information Technology
Rinabi Tanamal, Universitas Ciputra
Department of Information Technology


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How to Cite
Salsabila, D., & Tanamal, R. (2020). Design of Expert System for Digestive Diseases Identification Using Naïve Bayes Methodology for iOS-Based Application. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 5(2), 92-98.