DESIGN AND IMPLEMENTATION OF AN EXPERT SYSTEM IN DIAGNOSIS AND TREATMENT OF BREAST CANCER

CHAPTER ONE   
1.1  INTRODUCTION 
Over the year the leading cause of death in women aged between 35-54 years is breast cancer and second to cardio vascular disease in order women (Logan 1975). Mortality and incidence rate vary throughout the world with England and wale making first and us fourteen developing countries like Nigeria not left. In fact and two leading cancer sites are breast and lung accounting for about 25% of all cancer such a common disease in women and 1 and 12 women is affected in the U.S and 1 Nigeria. However its not surprising that there exist and long list of potential risk factor these include nutrition Geography Ethic factor aging factor, age of first birth being breast disorder decrease parity, unopposed ovarian cycling menstrual experience lactation experience, viral hypothesis however hereditary and family history of breast cancer are now considered to be two most important risk factor majority of women now come to medical attention in developing countries than in developing countries consequences the current emphasis is on prevention and early diagnosis of breast cancer so as to minimizes the death rate cause by it.   
1.2   STATEMENT OF THE PROBLEM 
The problem to be addressed in this work is the development expert system for the diagnosis of breast cancer artificial intelligence method are to the employed in the development of the system and sub problem can be stated as follow. 
(a)    The need for early diagnosis of breast cancer. 
(b)    The diagnosis of breast cancer require expert skill are short supply 
(c)    The need to reduced and mortally and incidence rate by breast cancer 
(d)    Need to improve the overall quality of health through early expert diagnosis of breast cancer.

1.3   PURPOSE OF THE STUDY 
1.     The study and apply AI technique in development of expert system 
2.     To show how knowledge can be acquired and represented in intelligence system. 
3.     To formulate an intelligence diagnosis system and easy diagnosis of breast cancer (dr cr) 
4.     To demonstrated the use of logic programmes as a logical tool for developing intelligent system. 
5.     To make a high level of expertise available to less qualified profession 
6.     To enhance the ability of leading expert in medical diagnosis. 
7.     To serve the knowledge and expertise of leading expert who eventually retire or die.   
1.4   MOTIVATION OF THE STUDY               
The need for solving complex diagnostic problem using AI method 
(a)    There is an increasing need to acquire and store the knowledge of human experts in automated reasoning system in order to assist the expert take decision faster and I the absence of the expert. This study is a in that direction. 
(b)    Developing generic diagnostic problem solving strategies. 
Diagnosis of breast cancer is chosen to demonstrate the application of AI technique in solving medical diagnostic problem. Breast cancer is a current topic issued in medicine since the cancer scourge assumed global important. However the motivation for this study is high not in diagnosis of breast cancer but also suggesting life expectancy and possible treatment of breast cancer and other related disease.       
METHODOLOGY 
The study involve computer based experiments requiring method discussed briefly.   
(a)    SYSTEM STUDY             
This involve elaborated study of the principle and techniques governing the development and operated of automated reasoning system for solving complex diagnosis problem the system is studies by learning its properties and concept through incremental understanding and knowledge base development and updating the inference engine reveal the pattern of reasoning adopted by the system to make intelligence decision.   
(b)    SYSTEM DESIGN             
AI methodologies for knowledge representations and inference will be applied in designing the system this method include. 
i.       Object the conclusion that is defined by associate rule 
ii.      Attribute a specific quality that with its rule help define the object 
iii.     Rules of inference.             
Knowledge base can however be a list of object their associated rule and attribute inference engine part of the knowledge based to found a situation that matches and its involve. 
i.       Forward changing techniques data base drive uses information that is provide to make through a network of logical and or until the it reaches the terminal point. 
ii.      Background chaining technique (object drive) reverse of forward chaining 
iii.     The rule value method request information that has the greatest importance according to the current state of the system. However backward changing technique is employed for this study reference from chapter two three.   
1.5   SYSTEM IMPLEMENTATION AND TESTING 
This study will be implemented with turbo prolong and c as a research logic programme tool and tested with actual data from hospital and patient with breast cancer.   
1.6   SYSTEM EVALUATION             
A system prototype or (software prototype) is first established the result of diagnosis generated from the system will be compared with already expect established diagnosis on patient with breast deviation from the with expected result then the system debugged until output correspond with expected results then the system can be analyze as perfect software water fall model from diagnosis and treatment of breast cancer.
1.7   ARTIFICIAL INTELLIGENCE (AI)             
Artificial means man made intelligence according to the dictionary means "the capacity to apprehend facts and prepositions and their relation and to reason about them intelligence is related to the ability to recognize patterns draw conclusion analyze complex system into simple elements and resolve contradiction. However, AI can be define as the science of making machine do things that would require intelligence if done by people (Alan Turning 1950). Moreso, AI can be defined as the incorporation of intelligent thinking programs into machine which are executed in solving knowledge intensive "specific" problems.