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:

1. 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.
2. To improve the existing manual system by considering pneumonia fuzzy input factors for accuracy in diagnosis for children.
3. 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;

1. Knowledge base
2. Fuzzification

• Fuzzy inference

1. Defuzzification.

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

1. A thorough review and swots of relevant literatures on fuzzy logic, expert systems and pneumonia diagnosis.
2. A thorough study and understanding of the existing system as well as gathering data through interaction with medical experts and knowledge gathering literatures.
3. Object oriented design tool like java for the expert system interface is employed for development of the expert system for pneumonia.
4. Embedded database was also employed for storing the rules.
5. 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

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

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

1. Health care provider: some who takes care or examines a patient.
2. 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.
3. 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.

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

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