Design and Implementation of Intelligent System for Detection and Analysis of Ebola Disease

Ebola virus disease is a hemorrhagic fever that has a near 100% fatality rate if not detected on time and properly managed. Between December 2013 and September 6, 2015, Africa and few other countries in the west witnessed the worst outbreak of the disease with 28,183 confirmed cases out of which 11,306 died. In an untiring effort to eradicate this pandemic, scientists have sought different measures for treating and caring for infected persons while also preventing further transmission of the disease. Hitherto, there still exist cases of transmission among humans especially patient-to-health care provider transmission. This project addresses the problem using visual programming language for diagnosing the disease. Requirement gathering exercise and specification was done through interviews with health care providers, site visit to Ebola treatment center and review of literature and Ebola registries. Expert system concepts with Visual Basic programming language were adopted in the development of the system. Reliable inferences were made regardless of the Ebola case scenario that was used in the testing of the expert system. The system showed that reduction in person-to-person transmission of Ebola virus disease can be achieved if probable suspects are identified and diagnosed on time using computer applications that eliminates physical contact with suspects or infected materials and fluids. For confirmed suspects, the system recommends laboratory test as a final proof of the infection. Using an interactive diagnosis expert system for detecting Ebola cases is a fast and safer avenue through which Ebola transmissions; especially human-to-human transmissions could be reduced.

Keywords: Ebola virus; Expert system; Machine learning; Intelligent system and disease detection