Abstract:
Personalized medical care is an individualized approach to the management and treatment of
diseases in the health care system. It follows the personalized medicine concept and has
received a great deal of recent attention from the governing, scientific and health care
diseases communities worldwide. Diabetes mellitus type 2(T2DM) is one of the chronic non communicable diseases that threaten different individuals and need a high cost of
expenditure if not managed effectively leads to other health complications. The main
objective of this study is to develop a Case-based reasoning system for personalized medical
care for patients with T2DM. Personalized medicine is speedily having an impact on how
patients are managed and treated; how health care delivery is channelling its resources to
maximize patient benefits. Management of T2DM consists of major lifestyle (dietary pattern),
drug therapy (administration) and physical exercise. To alleviate the disease complications,
the development case-based reasoning system that can be used to manage and treat patients
with T2DM through personalized medical care is essential. Additionally, curbing the
complication of T2DM can be facilitated with a Case-based reasoning system for
personalized medical care through a developed case base. To develop this prototype, design
science within knowledge engineering method and data gathering tools such as semi structured interviews and document analysis were followed. The domain experts were
selected by using the purposive sampling technique. The knowledge acquired from domain
experts and through document analysis was modelled by using the hierarchical conceptual
modelling method, and cases were generated and represented with the feature-value format.
The prototype was implemented by using JCOLIBRI software and scored an F-Measure of
84% and users acceptance of 86.2% and 82% respectively. The application of a case-based
reasoning system in personalized medical care is very important to improve the quality of
service delivery to address each patient’s case through unique characteristics/attributes such
as age, gender, blood pressure, fast blood sugar and others. Even if the porotype registered
promising results, future research work is expected from different scholars in personalized
medicine for more improvement.
