Abstract:
Malaria in sub Saharan countries especially in Ethiopian is the major one killer disease that can’t
pushed mainly in lowland areas. In Ethiopia the major challenge for healthcare service diagnosis
is shortage of skilled manpower. The number of health professionals and patients’ demand are
disproportionate according to Ethiopian ministry of health and WHO report. As a result several
lives have been lost while others are living with deteriorated health status. This research study had
developed artificial neural network and fuzzy logic soft computing techniques which provides an
efficient means of handling the complexity associated with the diagnosis of malaria. Both
techniques investigated to develop an intelligent decision support diagnosis to improve the ability
of physician. The study built artificial neural network (ANN) model which helps classifies malaria
patterns and fuzzy logic (FL) model which helps decision diagnosis support prediction. The ANN
model is a supervised learning techniques uses for classification of malaria dataset to their
classification level. A fuzzy logic model was developed using mamandi inferences engine
techniques to decide a decision making prediction process. The feature value of malaria dataset
diagnosis serve as the core input parameters to the ANN and FL. Experimental study of the
research study was conducted using medical records of malaria patients from Arba Minch Referral
hospital. The performance of the ANN study were evaluated using cross entropy, ROC and
confusion matrix. Fuzzy logic model was defuzzified using centroid of gravity defuzzifier.
