Decision Support Diagnosis System Using Artificial Neural Network and Fuzzy Logic Modeling in Case of Malaria

Abstract

Health problems touch every aspect of human life such as health condition, working environment, family life, social relations, economic and political activities of every endeavor. Malaria remains one of the world’s most deadly infectious diseases and arguably, the greatest threat to modern society in terms of morbidity and mortality. 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 a minster health. As a result of this complexity, several lives have been lost while others are living with deteriorated health status. This research proposes to develop 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 proposed study builds Artificial Neural Network (ANN) model which helps to classify malaria patterns and builds a Fuzzy Logic (FL) model which helps decision diagnosis support. The feature value of malaria dataset diagnosis serve as the core input parameters to the ANN and FL. Experimental study of the proposed study was conducted using medical records of malaria patients from Arba Minch Referral hospital and the results of the study were found to be within the range of predefined limit as examined by medical experts.