Abstract:
iseases that attack newborn babies within the first 28 days of their lives are referred to as
neonatal diseases. It is still a major health issue today because today’s medical diagnosis
depends on an understanding of human intelligence, which takes time and creates inconsistency
in decision-making. To solve the issue, integrating expert knowledge with technology is
required. Currently, knowledge-based systems have become a possible choice to support experts
in decision-making, regardless of the diagnosis of various diseases. Both ontology and case
based reasoning are types of knowledge based reasoning techniques used in this study to model
knowledge and design prototypes, respectively. The goal of this study is to develop an ontology-
enabled case based reasoning system that integrates ontology and case-based reasoning for the
diagnosis of newborn diseases. To provide ontology-enabled case based reasoning, ontology
was built independently and mapped to case based reasoning by using the Description logic
extension, Onto Bridge, and Jena connector. A design-science research method is followed to
structure the study. The knowledge needed for neonatal disease diagnosis was captured from
the neonate dataset, which was collected from Haramaya University Hiwot Fana Specialized
University Hospital. The collected dataset was preprocessed for duplication, cleaning, and
transformation using Weka and Excel. Ontology is constructed using the protégé tool and owl
language, while case based reasoning is designed using the Jcolibri framework. The protégé
takes the captured data and represents knowledge in an organized and structured manner. Pure
ontology is used to create case structure, and the ontology has been used in the case based
reasoning application as a case base. The prototype scored an average of 88% accuracy, with
precision and recall of 87% and 87%, respectively. Likewise, the user acceptance testing scored
89%, which shows the acceptability of the prototype by experts. This shows the prototype has
registered a positive outcome and was well accepted by the experts to develop an applicable
system to solve the problem. However, integrating DM results with the ontology to capture
hidden knowledge and automatically update the case base needs further enhancement in the
future
