Abstract
Malaria is endemic in Africa, though curable it is difficult to manage the prompt diagnosis of the disease because available diagnostic tools are affected by the harsh tropical weather. Also, the lack of electricity for the storage of current diagnostic tool in the rural areas as well as the fact that it has signs and symptoms that are similar to those of typhoid fever; a common disease in the region as well, is a major setback. This paper describes the research and development in implementing an Intelligent Decision Support System for the diagnosis of malaria and typhoid fever in the malaria subregions of Africa. The system will be mounted on a laptop, the one child per laptop, which will be powered by a wind-up crank or solar panel. The region chosen for our study was the Western Subregional network of malaria in Africa.
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Adehor, A.B., Burrell, P.R. (2008). An Intelligent Decision Support System for the Prompt Diagnosis of Malaria and Typhoid Fever in the Malaria Belt of Africa. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_28
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DOI: https://doi.org/10.1007/978-0-387-09695-7_28
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