Description

Despite the enormous experience accrued to physicians from their daily activities with patients suffering Ebola Virus Disease (EVD), it is observed that most physicians have difficulties in analyzing and processing complex health information. In most cases, multiple symptom presentations can be mistaken for other ailments scenarios where patients have multiple complaints can result in confusion to the consulting physician and if prompt analysis of the problem is not done, a decision on the treatment procedure to follow will not be done, especially the case of malaria and typhoid that has similar symptoms with Ebola. This could either lead to an aggravation of the problem or a loss of life.

The aim of this thesis is to develop a hybrid decision support system for the diagnosis of Ebola Virus Disease (EVD).

The Objectives of the study include:

i)                        To develop a Neural Network model for the training of the Dataset and a Genetic Algorithm model for Pattern Classification and Optimization.

ii)                      To develop a user friendly Graphical User Interface (GUI) to enhance interaction with Database.

iii)                    To compare the existing system with the proposed system.

iv)                    To implement the system using Java Programming Language.

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