Application of Bayesian Networks in Early Diagnosis of Cerebral Malaria and Mosquito-Borne Diseases Based on Observed Symptoms

Solomon Osarumwense Alile¹

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.6 , pp.1-14, Jun-2020

Online published on Jun 30, 2020

Copyright © Solomon Osarumwense Alile . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Cerebral malaria is an unstable central nervous system (CNS) affliction brought about by severe plasmodium falciparum infection that causes swelling of the cerebral vessels and venules with blood and water. The manifestations of the disease are chills, coma, convulsions, delirium, dizziness, fatigue, fever, headache, high temperature just to give some examples. This disease influences individual of any age however constant with kids underneath the age of 5. Because of the covering side effects of this malady, it was discovered that the disease is under-diagnosed and misdiagnosed a situation which is prevalent in Sub-Sahara Africa. Furthermore, the association of CNS with falciparum malaria accounted for 10% of admitted patients and 80% recorded passings around the world. Be that as it may, in time past, a couple of systems have been created to recognize this non-transmittable ailment, yet they delivered a ton of bogus negative during testing and couldn`t distinguish cerebral malaria in view of its covering symptoms it imparts to other mosquito-borne ailments. Consequently, there was the need to proffer a solution for the issue of under-diagnosis and misdiagnosis of cerebral malaria which is much uncontrolled in Sub-Sahara Africa. Hence, in this paper, we proposed and built up a model to anticipate cerebral malaria and mosquito-borne diseases using an AI method called Bayesian Belief Network. The model was structured using Bayes Server and tested with data retrieved from severe malaria medical repository. The model had an overall prediction exactness of 99.98%; 99.74%, 98.97% and 99.23% sensitivity of Cerebral Malaria, Malaria and Mosquito-Borne Disease correspondingly

Key-Words / Index Term :
Cerebral Malaria, Malaria, Mosquito-Borne Diseases, Diagnosis, Prediction, Detection, Machine Learning, Bayesian Belief Network

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