A Mobile Malaria Fever Clinical Diagnosis System Based on Non-Nested Generalized Exemplar (NNGE)

Abstract

© 2020, World Academy of Research in Science and Engineering. All rights reserved. The world today is a world of internet technology which has been deployed in all fields of endeavours to change the way humans do their daily activities and turn things around positively. The whole world is full of smart phones that house various mobile applications. A mobile application for malaria diagnosis was developed in this work to provide a wider coverage and accessibility. A data mining technique-Non-Nested Generalized Exemplar (NNGE) was used on malaria labelled dataset collected from a reputable hospital in Ado-Ekiti, Ekiti State, Nigeria to generate a classification model for malaria diagnosis. The model was tested on both the training and testing sets, attaining 100% and 98.04% detection rates respectively. The rules generated by NNGE were used as the inference engine of the user friendly mobile diagnosis application. The mobile app was developed using Java, HTML and PhP as front end, MySQL as the backend and Apache as the webserver.