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1.1 Background of the Study
An expert system is a software system that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are
2) A process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.
As a premiere application of computing and artificial intelligence, the topic of expert systems has many points of contact with general systems theory, operations research, business process reengineering and various topics in applied mathematics and management science.
Two illustrations of actual expert systems can give an idea of how they work. In one real world case at a chemical refinery a senior employee was about to retire and the company was concerned that the loss of his expertise in managing a fractionating tower would severely impact operations of the plant. A knowledge engineer was assigned to produce an expert system reproducing his expertise saving the company the loss of the valued knowledge asset. Similarly a system called Mycin was developed from the expertise of best diagnosticians of bacterial infections whose performance was found to be as good as or better than the average clinician. An early commercial success and illustration of another typical application (a task generally considered overly complex for a human) was an expert system fielded by DEC in the 1980s to quality check the configurations of their computers prior to delivery. The eighties were the time of greatest popularity of expert systems and interest lagged after the onset of the AI Winter.
In like manner, developing one of such system to represent the repository of the knowledge of a medical doctor is as essential as any other expert system. To this end, this project, Expert System on the Diagnosis of non communicable diseases is a necessity.
1.2 Statement of the Problem
Health care facility should be accessible by all at all times. But some of the people that should access these facilities are far removed from these facilities. More so, in the few available facilities, qualified medical personnel are always key issues that need urgent redress.
In view of the foregoing, it would be of great necessity to provide a computerized system that will provide a complementary medical service, such as medical disease diagnosis in places where accessibility is a problem as well as health care facilities where qualified experts are lacking, hence this topic, Expert System on Malaria and typhoid fever Diagnosis.
1.3 Objectives of the Study
The major objective of this work is to develop an expert system ondiagnosis of non communicable diseases. It also targets towards contributing to academic research work.
It is also to ascertain whether the diseases could be diagnosed based on signs and symptoms.
It will also examine a patient based on simple clinical signs, and to improve family and community health
1.4 Significance of the Study
If this prototype is fully developed will be very useful in many areas such as:
a. It will help to retain the skill of an expert medical doctor in case of any eventuality;
b. It can support academic development;
c. It can be useful in many hospitals, both private and government, cases where the expert is not on seat;
d. It can also be used in the laboratory for quick research work.
1.5 Scope of the Project
The scope of this work will include the following
1. A dynamic database system that can act as a knowledge based system
2. A complete model of an expert system to portray sample troubleshooter
3. Explore the power of visual basic in data handling
4. To implement a dynamic search system
1.6 Limitations of the Study
The major constraint faced during the implementation of this work was finance. This is among other frustrations such as program failures during modular construction stages. Time was another important factor that limited the extent to which I want to research.
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