Abstract:
Malnutrition is an umbrella term for the unsuitable intake of nutrients needed to sustain healthy
development; the term applies to under or over nutrition. It is possibly known to be one of the
predominant causes of illness and death for under-five children in Ethiopia. Some of the
reasons which worsen the spread of malnutrition in the country are: lack of specialists,
practitioners and health facilities at lower level health institutions in order to diagnose and
give treatment at early stage. Artificial Intelligence, which uses computer applications by
simulating human intelligence, was applied in the research especially for malnutrition
diagnosis. The general objective of this study was to design a case based reasoning system that
provides expert advice for diagnosis of malnutrition under five year children. The cases were
collected from both Jimma University specialized hospital and Hawasa university
comprehensive specialized hospitals and design science were followed to design prototype case
based reasoning system. Stratified sampling technique was employed to select domain experts
for knowledge acquisition and for system testing and evaluation from Jimma University
specialized hospital. For the development of the prototype system, the researcher used
jCOLIBRI version 1.1 implementation tools and nearest neighbor algorithm. Evaluation of the
developed prototype was performed for both system performance and user acceptance. For
testing of the prototype seven test cases and six domain experts were used. Based on evaluating
the performance of the system, the average precision and recall values achieved were 71% and
83% respectively. User acceptance testing also performed by involving domain experts and an
average of 83% acceptance was achieved. Insertion of additional cases could increase the
performance of the CBR system. In this study a promising result was obtained and met the
objectives of the study.
