Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System
No Thumbnail Available
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Computational Science and Its Applications – ICCSA 2020
Abstract
In the process of clarifying whether a patient or patients is suffering
from a disease or not, diagnosis plays a significant role. The procedure is quite
slow and cumbersome, and some patients may not be able to pursue the final test
results and diagnosis. The method in this paper comprises many fact-finding and
data-mining methods. Artificial Intelligence techniques such as Neural Networks
and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy
Expert System diagnostic tool. The authors conducted oral interviews with the
medical practitioners whose knowledge were captured into the knowledge based
of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software
was implemented with Microsoft Visual C# (C Sharp) programming language
and Microsoft SQL Server 2012 to manage the database. Questionnaires were
administered to the patients and filled by the medical practitioners on behalf of
the patients to capture the prevailing symptoms. The study demonstrated the
practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid
learning rule has greatly enhanced the proposed system performance when
compared with existing systems where only the back-propagation learning rule
were used for implementation. It was concluded that the diagnostic expert
system developed is as accurate as that of the medical experts in decision
making. DIAGMAL is hereby recommended to medical practitioners as a
diagnostic tool for malaria.
Description
Keywords
Fuzzy inference system, Diagnosis, Expert system, Neuro-fuzzy modeling, Malaria