Author(s)

Grace Tam-Nurseman¹, Philip Achimugu², Oluwatolani Achimugu³, Hilary Kelechi Anabi³, Sseggujja Husssein⁴

¹Department of Computer Science, Lead City University, Ibadan, Nigeria.
²Department of Computer Science, Air Force Institute of Technology, Kaduna, Nigeria.
³Department of Information and Communication Engineering, Air Force Institute of Technology, Kaduna, Nigeria.
⁴Department of Computer Science, Islamic University in Uganda, Mbale, Uganda.

Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.

Expert System, Dental Diagnosis, Dental Diseases and Human Expert

Tam-Nurseman, G. , Achimugu, P. , Achimugu, O. , Anabi, H. and Husssein, S. (2021) Expert System for the Diagnosis and Prognosis of Common Dental Diseases Using Bayes Network. Journal of Biomedical Science and Engineering, 14, 361-370. doi: 10.4236/jbise.2021.1411031.