1.1 Introduction
In recent years, expert systems have emerged as pivotal tools in the advancement of medical care globally, including in Nigeria. Expert systems, which are a branch of artificial intelligence, simulate the decision-making ability of a human expert. They have the potential to revolutionize healthcare delivery by improving diagnostic accuracy, treatment plans, and patient management. In Nigeria, where healthcare resources are often limited and unevenly distributed, the integration of expert systems holds significant promise for bridging gaps in medical expertise and access to quality care. The implementation of expert systems in Nigeria's medical sector can enhance healthcare outcomes by providing decision support to medical practitioners, optimizing resource allocation, and facilitating the management of large volumes of patient data. For instance, these systems can aid in diagnosing diseases by analyzing patient symptoms and medical histories, suggesting potential diagnoses, and recommending appropriate tests and treatments. This is particularly valuable in rural areas where specialized medical personnel may be scarce.
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitation of the study and Definition of technical terms.
1.2 Background of Study
Expert system play an indomitable role in various disciplines and the assistance it renders to man cannot be overemphasized. The development of experts system that capture skills and expertise of a human expert has not been easy and date back to the Second World War, where experts of computer tried relentlessly to develop technique or systems that would allow act more intelligently than human. But recently, experts system have proven their value by aiding for example, medical professionals in diagnosing diseases and scheduling task as well mechanics in trouble shooting locomotive problems and configuring complex computer hardware.
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 the creation of a so-called “knowledgebase” which uses some knowledge representation formalism to capture the subject matter experts (SME) knowledge and 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.
1.3 Statement of Problems
Investigation revealed that expert systems rely on large datasets and sophisticated algorithms to function effectively. However, the lack of comprehensive and digitized medical records in Nigeria poses a significant challenge. Many healthcare facilities still rely on paper-based records, which are not only inefficient but also difficult to integrate into digital expert systems (Adedeji & Abolarinwa, 2020). Furthermore, there are concerns about data privacy and security, which need to be addressed to protect patient information and build trust in these systems.
Furthermore, the successful implementation of expert systems requires healthcare professionals who are not only skilled in their medical fields but also trained in using these advanced technologies. Unfortunately, there is a significant gap in the technical knowledge and training of Nigerian healthcare workers. Many medical professionals lack the necessary training to operate expert systems effectively, which can lead to underutilization or misuse of these tools (Ogunyemi et al., 2020).
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.
1.4 Aim and Objective of study
The aim of this study is to develop an expert system on diagnosis of non communicable diseases and assess the medical advancement in Nigeria. In achieving this aim, the following specific objectives were laid out as follows:
- To investigate how expert systems can improve diagnostic accuracy and treatment outcomes in various medical fields;
- To assess the readiness of healthcare institutions and professionals to integrate and utilize expert systems effectively;
- To evaluate the existing healthcare infrastructure and its capacity to support advanced technological solutions;
- To examine the Potential Benefits of Expert Systems in Healthcare; and
- To develop training programs for healthcare professionals to enhance their technical skills and familiarity with expert systems.
1.5 Significance of the study
The primary significance of this study lies in its potential to improve healthcare delivery in Nigeria. Expert systems can enhance diagnostic accuracy, optimize treatment plans, and facilitate better patient management, particularly in resource-constrained settings. Also, the adoption of expert systems represents a significant technological advancement in the Nigerian healthcare sector.
Additionally, the findings of this research will also highlight the importance of integrating modern technology into healthcare practices, promoting innovation, and encouraging the development of homegrown technological solutions tailored to Nigeria's unique healthcare needs (Adebola & Olamide, 2021).
Furthermore, this study underscores the need for robust policy and regulatory frameworks to support the integration of expert systems in healthcare. Clear guidelines and standards are essential to ensure the safe and effective use of these technologies. The study's findings can inform policymakers on the necessary regulatory measures, fostering an environment conducive to technological innovation and adoption in healthcare (Eze, 2019).
1.6 Scope of the Study
The scope of this research is focused on the expert systems and medical advancement in Nigeria.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Establishment Policies: Establishment policies posed a serious limitation as most staffs are not ready to release information needed for this project work. There were lots of information needed from the staffs of this institution to enhance the study which took them time to release or they did not release at all for security purposes, hence the scope was reduced.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
1.8 Definition of Terms
Symptoms: Is the sign given when a particular thing want to happen
Diagnosis: The act or process of identifying or determining the nature and cause of a disease injury through evaluation, examination review of a patient laboratory data
Medication: A medication is a substance that is taken into or placed on the body that does one of the following things: most medications are used to cure disease or condition. (For example, antibiotics are given to cure an infection. Medications are also given to treat a medical condition).
