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Please use this identifier to cite or link to this item:
http://repository.fuoye.edu.ng/handle/123456789/1480
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| Title: | DEVELOPMENT OF AN INTELLIGENT DECISION SUPPORT SYSTEM FOR PROMPT DIAGNOSIS OF EBOLA AND LASSA FEVER DISEASE |
| Authors: | ADE-OJO, TOLUWANI |
| Keywords: | DEVELOPMENT
INTELLIGENT
SUPPORT
DECISION
EBOLA
LASSA
FEVER
DISEASES |
| Issue Date: | 12-Nov-2018 |
| Publisher: | FEDERAL UNIVERSITY OYE EKITI |
| Citation: | Ayten Kadanali and GulKaragoz (2015), ‘An Overview of Ebola Virus Disease’. Northern Clinics of Instanbul, Vol. 2(1), pp: 81–86, https://10.14744/nci.2015.97269. |
| Series/Report no.: | CSC/14/2209; |
| Abstract: | Ebola Virus Disease (EVD) and Lassa fever are infectious viral diseases that are very deadly to
mankind. These diseases, when handled lightly can quickly degenerate into deadly epidemics.
Accurate and prompt diagnosis, and effective treatment of these infectious diseases is a very
critical factor in their prevention and containment. The difficulty in differentiating between EVD
and Lassa fever at their initial phase can result in wrong diagnosis which can be catastrophic. An
intelligent decision support system can help make faster and more accurate diagnosis of these
diseases. |
| Description: | The major challenge facing the healthcare industry is the provision of quality services at affordable
costs (Obanas, 2013). A quality service implies diagnosing patients correctly and treating them
effectively (John, 2009). Poor clinical decisions can lead to disastrous results which is
unacceptable. Medical diagnosis is known to be subjective, that is, it depends on the physician
making the diagnosis (Resul, and Abdulkadir, 2008). Secondly, and most importantly, the amount
of data that should be analyzed to make a good prediction is usually huge and at times
unmanageable. In this context, machine learning can be used to automatically infer diagnostic rules
from descriptions of past, successfully treated patients, and help specialists make the diagnostic
process more objective and more reliable (Polat and Gunes, 2007) |
| URI: | http://repository.fuoye.edu.ng/handle/123456789/1480 |
| ISSN: | CSC/14/2209 |
| Appears in Collections: | Computer Science Thesis
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