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Full Project – Expert system for the diagnosis of pneumonia in children

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CHAPTER ONE

INTRODUCTION

Pneumonia, a disease characterized by inflammation of the lungs continues to be a number one killer of children in the world. According to the United Nation Children’s Fund (UNICEF), it was estimated that pneumonia causes 18% of all child mortality an estimated of 1.3 million child deaths in 2011 alone. Pneumonia kills more children than any other illness more than AIDS, malaria and measles combined. Yet, little attention is paid to this disease. Majority of pneumonia cases are preventable and treatable and more than 99% of pneumonia death occur in developing countries which Nigeria is a part of (UNICEF, 2006).

Pneumonia can occur when the lungs are exposed to germs not usually present in the lungs. Although pneumonia was referred to as the captain of the men of death (William Osler, 1901), the introduction of antibiotics therapy and vaccines in the 20^th century has been of great help to reduce death rate. The lungs may have been exposed to a large amount of virus/bacteria or an individual must have been ill for example, with flu or cold and so the immune system is weakened. Some common symptoms of pneumonia include fever, cough, shortness of breath, chest pain, especially when one takes a breath, coughing up mucus, sometimes blood-stained.

A typical pneumonia is diagnosed by a health care provider by reviewing the symptoms and examining the individual. The health care provider often check for fever, breathing problems and makes the patient undergo series of test like the chest x-ray, blood tests, lab tests of a sputum sample. Thereafter, the healthcare provider will determine the medicine the individual needs. Since medical diagnosis deals with fuzziness, a fuzzy logic approach is best suitable for such diagnosis (Akinwale, 2008).

The proposed fuzzy expert system for pneumonia diagnosis is a stand-alone system designed to impact decision making about individual patients at a particular point in time in the diagnosis of pneumonia. A fuzzy expert system generally is a collection of fuzzy rules and membership functions that can be used to reason about data (Zadeh, 1965). Here, the fuzzy expert system diagnose pneumonia based on it knowledge, do ranking and gives the result in a fuzzy form. Fuzzy expert systems are generously accepted in every sphere of life.

Statement of Problem

Diagnosing pneumonia can be complex a times. The analysis and deduction carried out by the healthcare practitioners based on his/her experience with patient can be challenging to him. There can be likelihood of errors based on his decisions since he is dealing with fuzzy data.

Whenever it comes to diseases, time plays a vital role. The time it takes a medical practitioner to reason and make his decisions is also another problem that will not be over-looked. The more serious a pneumonia case is, the more time it will take a medical practitioner to reason and make his deductions which leads to his final decisions.

Medical doctor’s availability is another problem the manual system is currently facing. There are hospitals with just one medical doctor, and this means that if the doctor is not around, the patient will have to wait for him/her or come back another day in order to see the doctor to begin his/her diagnoses. This can be a serious issue especially in severe cases of pneumonia.

Aim and Objectives of the Project

The aim of this study is to develop a fuzzy expert system for diagnosis of Pneumonia in children using the Mamdani Inference System. The objectives of the study also include:

To design a system that is capable of imitating the expertise of a domain expert, thereby evaluating the pneumonia degree level in a patient based on it symptoms.
To improve the existing manual system by considering pneumonia fuzzy input factors for accuracy in diagnosis for children.
To design Mysql database system to store information about the decision variables that can support fast, reliable and accurate diagnosis with limited and vague information.
- Research Methodology

A fuzzy expert system will be developed for the diagnosis of pneumonia which uses a fuzzy logic approach and will be designed as a rule base expert system. A rule base expert system is one whose knowledge base contains the domain knowledge coded in the form of rules.

The main components of our system are;

Knowledge base
Fuzzification

Fuzzy inference

Defuzzification.

In achieving the objectives of this project, the following methods will be adopted;

A thorough review and swots of relevant literatures on fuzzy logic, expert systems and pneumonia diagnosis.
A thorough study and understanding of the existing system as well as gathering data through interaction with medical experts and knowledge gathering literatures.
Object oriented design tool like java for the expert system interface is employed for development of the expert system for pneumonia.
Embedded database was also employed for storing the rules.
MATHLAB is also used for establishing the membership function.
- Scope and Limitation

This system is designed to work online in a medical Centre. The procedures in pneumonia diagnosis were carefully observed and some deductions were made, which limits this research to the general understanding of the problem. The system will assume that there is an existing patient’s database which implies that the system will not create, edit or update a patient’s database management system.

1.5 Significance of Study

This project will help patient undergo prompt pneumonia diagnosis whether the medical doctor is around or not. The expert system will also offer assistance to medical practitioners and healthcare sector in making prompt decision during the diagnosis of pneumonia as well as reduce traffic intensity in seeing a medical doctor. This project will also provide researchers up-to-date information whose interest is fuzzy logic and expert system.

1.6 Organization of Project

This project is organized into five chapters:

Chapter one deals with the general introduction that provides a brief background of the project of the study. In Chapter two, the literature review of related articles to the study is discussed. Chapter three deals with the System analysis and design of the diagnosticsystem using fuzzy logic. Chapter four explains the implementation details of the study. Chapter five concludes with the summary, recommendation and conclusion, references.

1.7 Definition of Terms

Diagnosis: The process identification of an illness or problem by examination of Symptoms.
Symptom: A sign that shows that a disease is suspected or actually present.

Patient: A person being watched or examined by a medical practitioner.

Health care provider: some who takes care or examines a patient.
Artificial Intelligence (AI): It is a field in computer science and engineering that is concerned with building intelligent systems that can engage on behaviors that human consider intelligent.
Expert System: This is a computer program that imitates the thought process, reasoning, and decision making capability of humans and provides expert advice on a narrow problem domain.

Fuzzy logic: This is an approach to computing based on marginal truth rather than Boolean logic.
Fuzzification: This is a process that determines the degree of membership to the fuzzy set based on fuzzy membership function.

Defuzzification: This involves changing fuzzy output back into numerical values for system action.

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