Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Modu, Babagana, Polovina, Nereida, Lan, Yang, Konur, Savas, Asyhari, A Taufiq and Peng, Yonghong (2017) Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System. Applied Sciences, 7 (8). p. 836.

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Abstract

Malaria, as one of the most serious infectious diseases causing public health problems in
the world, affects about two-thirds of the world population, with estimated resultant deaths close
to a million annually. The effects of this disease are much more profound in third world countries,
which have very limited medical resources. When an intense outbreak occurs, most of these
countries cannot cope with the high number of patients due to the lack of medicine, equipment and
hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging,
especially in third world countries, due to poverty and economic insatiability. Technology can
offer alternative solutions by providing early detection mechanisms that help to control the spread
of the disease and allow the management of treatment facilities in advance to ensure a more
timely health service, which can save thousands of lives. In this study, we have deployed an
intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria
outbreak based on climatic factors using machine learning algorithms. The system will help hospitals,
healthcare providers, and health organizations take precautions in time and utilize their resources in
case of emergency. To our best knowledge, the system developed in this paper is the first publicly
available application. Since confounding effects of climatic factors have a greater influence on the
incidence of malaria, we have also conducted extensive research on exploring a new ecosystem
model for the assessment of hidden ecological factors and identified three confounding factors that
significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application;
this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

Item Type: Article
Uncontrolled Keywords: Data science, Machine learning. Bioinformatics, Health Informatics
Subjects: Computing > Artificial Intelligence
Computing > Information Systems
Divisions: Faculty of Computer Science
Depositing User: Yonghong Peng
Date Deposited: 20 Sep 2017 14:17
Last Modified: 23 Sep 2017 03:57
URI: http://sure.sunderland.ac.uk/id/eprint/7825

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