Design and Implementation of a Medical Expert System.
ABSTRACT
Diseases must be treated healthy and on time. If they are not treated on time, they can escort to many health problems with these problems might become the reason of death.
These problems are becoming inferior due to the scarcity of specialists, health facilities and practitioners.
In an attempt to address such problems, studies ended attempts toward design and develop expert systems which can present advice for physicians and patients to make easy the diagnosis along with recommend treatment of patients.
This review paper represents a comprehensive study of medical expert systems used for diagnosis of various diseases. It provides a concise overview of medical diagnostic expert systems along with presents an analysis of already existing studies.
TABLE OF CONTENTS
TITLE PAGE i
CERTIFICATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
TABLE OF CONTENTS vi
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER ONE: INTRODUCTION
1.1 BACKGROUND OF THE STUDY 1
1.2 STATEMENT OF THE PROBLEM 2
1.3 AIM AND OBJECTIVES OF THE STUDY 2
1.4 METHODOLOGY 3
1.5 SIGNIFICANCE OF THE STUDY 3
1.6 SCOPE OF THE PROJECT 3
1.7 LIMITATION OF STUDY 4
1.8 DEFINITION OF TERMS/VARIABLES 4
CHAPTER TWO: LITERATURE REVIEW
2.0 INTRODUCTION 6
2.1 OVERVIEW OF EXPERT SYSTEM 6
2.2 MEDICAL EXPERT SYSTEMS 6
2.3 STRUCTURE OF MEDICAL EXPERT SYSTEMS 7
2.4 EXISTING MEDICAL EXPERT SYSTEMS 8
2.4.1 EXPERT SYSTEM FOR DIAGNOSIS OF INFLUENZA UNDER UNCERTAINTY 9
2.4.2 FUZZY EXPERT SYSTEM FOR DIAGNOSIS OF RISK OF HYPERTENSION 9
2.4.3 RULE BASED EXPERT SYSTEM FOR MEMORY LOSS DISEASE 10
2.4.4 FUZZY SYSTEM FOR DIAGNOSIS OF LIVER DISORDER 10
2.4.5 ARTIFICIAL NEURAL NETWORK FOR DIAGNOSIS OF HEART DISEASE 11
2.5 APPLICATION OF EXPERT SYSTEMS 11
2.5 HISTORY OF DELACK HOSPITALS 11
2.6 ORGANOGRAM OF DELACK HOSPITALS 12
CHAPTER THREE: RESEARCH METHODOLOGY
3.0 INTRODUCTION 13
3.1 ANALYSIS OF THE EXISTING SYSTEM 13
3.2 OVERVIEW OF THE PROPOSED SYSTEM 14
3.3 METHOD OF DATA COLLECTION 14
3.4 SYSTEM DESIGN 15
3.4.1 FLOWCHART 15
3.4.2 UML DIAGRAM 18
3.5 DATABASE STRUCTURE 19
3.5.1 OUTPUT STRUCTURE 19
3.5.2 INPUT STRUCTURE 20
CHAPTER FOUR: SYSTEM TESTING AND IMPLEMENTATION
4.0 INTRODUCTION 22
4.1 CHOICE OF PROGRAMMING LANGUAGE AND DATABASE 22
4.2 SYSTEM REQUIREMENT 22
4.2.1 HARDWARE REQUIREMENT AND SPECIFICATIONS 22
4.2.2 SOFTWARE REQUIREMENT AND SPECIFICATIONS 23
4.3 RESULT INTERFACE 23
4.4 SYSTEM TESTING AND IMPLEMENTATION 25
4.5 DESCRIPTION OF FINDINGS 27
4.6 SYSTEM DOCUMENTATION 28
4.6.1 HOW TO LOAD THE SOFTWARE 28
4.6.2 HOW TO RUN THE SOFTWARE 28
4.6.3 THE PLATFORM 28
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 SUMMARY 29
5.2 CONCLUSION 29
5.3 RECOMMENDATION 29
REFERENCES 31
INTRODUCTION
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.
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 premier 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.
REFERENCES
Eugena & George (2009). The Diagnosis of Some Kidney Diseases in a PROLOG Expert System. Retrieved from https://www.researchgate.net/profile/Eugene_Roventa/publication/224593142_The_diagnosis_of_some_kidney_disease_in_a_small_prolog_Expert_System.pdf on June 27, 2018
Jimmy S (2013). The Diagnosis of Some Lung Diseases in a PROLOG Expert System. Retrieved from https://www.researchgate.net/publication/303699292_Implementation_of_an_Expert_System_for_Lung_Disease_Diagnosis on June 17, 2018.
Sammy, S & Abu, N (2008). An Expert System for Diagnosing Eye Diseases Using Clips. Retrieved from https://www.researchgate.net/publication/274258367_AN_EXPERT_SYSTEM_FOR_DIAGNOSING_EYE_DISEASES_USING_CLIPS on June 29, 2018
Hossain et al (2014). Expert System for Diagnosis of Influenza under Uncertainty. Retrieved from https://www.academia.edu/8752752751/A_Belief_Rule-Based_Expert_System_to_Diagnose_Influenze on June 20, 2018
Azian et al (2001). Fuzzy Expert System for Diagnosis of Risk of Hypertension. Retrieved from https://pdfs.semanticscholar.org/b7ad/d498b2927d35640946c249158ede32d33f21f.pdf on July 1, 2018
Komal et al (2014). Rule Based Expert System for Memory Loss Disease. Retrieved from https://www.ijset.com/v1s3/IJISET_V1_13_12.pdf on July 2, 2018
Neshat et al (2008). Fuzzy Expert System Design for Diagnosis of Liver Disorders proceedings of Knowledge Acquisition and Modeling. Retrieved from https://www.researchgate.net/publication/228814316_Fuzzy_Expert_System_Design_for_Diagnosis_of_Liver_Disorder on June 27, 2018
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