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Studies show that fuzzy logic has different approaches for enhancing personal health care delivery in the health care sector. Currently, breast cancer rated as the second leading cause of death among women, according to the World Health Organisation. Previous studies relating to breast cancer prognosis using fuzzy logic approach were directed at reoccurrence of the disease as well as the survivability of individual. However, there is need for early identification of the predisposing risk factors to breast cancer growth and its elimination. Consequently, this study focuses on developing an efficient Mobile-based Fuzzy Expert System (MFES) for initial breast cancer growth prognosis that can predict the individual risk level and thus, reduce the high incidence rate.
Facts relating to the predisposing factors of breast cancer were elicited from four domain experts through direct contact; this was used to generate the appropriate fuzzy rules. The fuzzy inference approach was employed to formulate the membership functions and fuzzy rules to design the MFES. Mamdani approach was used for the fuzzification of input and de-fuzzification of the output. The system accommodates imprecision tolerance and uncertainty to achieve tractability, robustness and least solution cost. Java expert system shell running on Android operating system was used to achieve the mobile technology aspect of the system. For the purpose of system evaluation, 2500 data were collected from two health care centers in Nigeria using random sampling technique. The data were stratified into twenty-five different strata. Each stratum contained 100 dataset and four individual data were selected at random.
The result indicated that the fact elicited from the experts serves as range values for the 12 risk factors used for the fuzzification of the input and thus, 36 rules were generated. The rules were used as basis for the development of the MFES. The developed MFES for breast cancer growth prognosis recorded 96% accuracy for all dataset picked from the 25 different strata. The system showed that with higher number of fuzzy rules focusing on pre-tumour growth and detailed predisposing risk factors; the prediction of risk level was reliable.
This work provided the resource for an individual to personally examine the breast cancer risk level, showing that the predisposing risk factors can be reduced by personal health monitoring. Though, MFES employed higher number of fuzzy rules unlike the existing systems with less number of rules; it supports pre-tumor growth instead of post-tumor growth which was incapable of handling the high incidence rate. It is therefore recommended that MFES be used to detect predisposing breast cancer risk levels early enough. The main contribution of this work is the reduction of the incidence rate in contrast to the existing methods currently applied in the diagnosis of breast cancer.
Keywords: Soft Computing, Fuzzy Set, Breast Cancer, Risk Factors, Membership Functions
Word Count: 448
TABLE OF CONTENTS
Title Page i
Table of Contents vi
List of Tables ix
List of Figures x
CHAPTER ONE: INTRODUCTION
1.1 Background to the Study 1
1.2 Statement of the Problem 3
1.3 Objective of the Study 4
1.4 Methodology 4
1.5 Justification for the Study 5
1.6 Significance of the Study 5
1.7 Scope of the Study 6
1.8 Operational Definition of Terms 6
1.9 Biblical Implication of Disease 7
CHAPTER TWO: REVIEW OF LITERATURE
- Introduction 8
- Mobile Computing 8
2.1.1 Mobile Applications for Personal Health Care Monitoring 9
2.1.2 Benefits of Mobile Apps for Personal Health Care Monitoring 10
2.1.3 Android Mobile Application using Java Expert system shell 10
2.2 The Current State of Art of Digital Technologies in the Health and Care Sector 11
2.3 Soft Computing 16
2.3.1 Computing with words 18
2.4 Fuzzy logic 19
2.4.1 Fuzzy Logic Theory 19
2.4.2 Fuzzy Logic in Breast Cancer 20
2.5 Fuzzy Inference System 21
2.5.1 The Structure of Fuzzy Inference Systems 22
2.6 Fuzzy Sets 26
2.6.1 Logical Operation on fuzzy set 29
2.7 Other Relevant Issues in Fuzzy Inference System 29
2.7.1 Linguistic variables and Fuzzy Rules 29
2.7.2 Membership Function 31
2.8 Breast Cancer 35
2.8.1 Risk Factors 35
2.8.2 Facts and figures about Cancer 37
2.9 Related Works 38
2.10 Summary 41
CHAPTER THREE: METHODOLOGY
3.0 Introduction 43
3.1 Mobile-based Fuzzy Expert System for Breast Cancer Growth
Prognosis design 43
3.2 Breast Cancer Prognosis Features Specification 44
3.3 Design stages of the Mobile-based Fuzzy Expert System 45
3.3.1 Knowledge representation in JESS 48
3.3.2 Input Variables 55
3.3.3 Fuzzy Rule-based System Specification 62
3.3.4 Methods for obtaining fuzzy rules 62
3.3.5 Rules for the Mobile-base Fuzzy Epert System for breast cancer growth
3.3.6 Structure of the Mobile-base Fuzzy Epert System for breast cancer
growth prognosis 65
3.3.7 The Aggregation of the outputs rule 66
3.4 Data Collection 70
3.5 Unified Modeling Language 71
3.5.1 Use case Diagram 73
3.5.2 Class Diagram 73
3.5.3 Sequence Diagram 73
3.6 Algorithm for the MFES 76
3.7 Ethical consideration 79
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND
DISCUSSION OF FINDINGS
4.0 Introduction 80
4.1 Development Tools 80
4.2 Designed System Specification 81
4.3 Design Considerations 81
4.3.1 Fuzzy Modelling 81
4.3.2 Evolving fuzzy systems for the Breast Cancer Prognosis 83
4.3.3 Fuzzy system parameters 83
4.4 System Implementation 85
4.5 Implementation Details for the designed MFES for breast cancer
4.5.1 The MFES User Interface 85
4.6 Experimental Result 92
4.7 Discussion 92
4.7.1 Performance Evaluation of the designed system 96
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary 98
- Conclusion 99
5.3 Recommendations 100
- Contributions to Knowledge 100
5.5 Suggestion for Further Studies 101
Appendix I: Breast Cancer Data set 111
Appendix II: Questions asked to collect data from healthy individuals 117
Appendix III: Codes for the designed Mobile Based Fuzzy Expert System 118
LIST OF TABLES
3.1 The requirement of breast cancer risk valuation and their descriptions 47
3.2 Experts Rating for the Indicators (Risk Factors) of the Breast Cancer disease 51
3.3 Range values for the fuzzified output 58
4.1 Parameter classification of fuzzy inference systems 82
4.2 Comparison of mobile based fuzzy expert system for Breast Cancer
Prognosis with other related soft computing approach 93
LIST OF FIGURES
2.1 Ginger.io 13
2.2 Digital Technologies in the health and care sector 14
2.3 Wearable Body Sensor Network 15
2.4 Structure of Fuzzy System Process 23
2.5 A Basic fuzzy Set of Triangular and Trapezoidal Membership Function 32
2.6 The general membership function curve. 34
3.1 The Mobile-based Fuzzy Expert System (MFES) model process 46
3.2 Triangular MF for age 59
3.3 Triangular MF for AFMC 59
3.4 Singleton MF for ALMC 60
3.5 Triangular MF for AFP 60
3.6 Trapezodial Membership function for Body Mass Index 61
3.7 Architectural framework of the Mobile-based Fuzzy Expert System
for breast cancer growth prognosis 67
3.8 The inference engine cycles showing a match-fire procedure 68
3.9 Rule execution cycle 69
3.10 Use Case diagram for the designed MFES 72
3.11 Class diagram for the designed MFES 74
3.12 Sequence diagram for the designed MFES 75
3.13 Flowchart for the designed MFES 78
4.1 The first input section of the designed MFES 86
4.2 The second input section of the designed MFES 86
4.3 The processing page of the designed MFES 87
4.4 The output section of the designed MFES 87
4.5 The output section of the designed MFES 88
4.6 The output section of the designed MFES 88
4.7 Graphical representation of risk factors fuzzification showing each level
of membership degree (First data in Appendix I) 89
4.8 Graphical representation of risk factors fuzzification showing each level
of membership degree (Second data in Appendix I) 89
4.9 Graphical representation of risk factors fuzzification showing each level
of membership degree (Third data in Appendix I) 90
4.10 Graphical representation of risk factors fuzzification showing each level
of membership degree (Fourth data in Appendix I) 91
4.11 Diagramatic representation of performance evaluation 95
- I. Breast Cancer Data set
- II. Questions asked to collect data from healthy individuals
III Codes for the designed Mobile Based Fuzzy Expert System
1.1 Background to the Study
Information and Communication Technology (ICT), specifically mobile health (mHealth), can play a key role in enhancing and enabling health care systems, when linked to specific needs. The initiation of various types of mobile portable computer devices – smartphones, private digitally powered assistants, and tablet systems has influenced an appreciated positive impact in many works of life which includes the health sector. This has been influenced by the increasing excellence and availability of application software in the health sector, (Aungst, 2013). These softwares are set of instructions that have been written in a particular programming language to run on a moveable portable aid or on a computer system to achieve a particular purpose, (Wallace, Clark & White, 2012). In recent development faster processors and improved memory in the analysis of complex data in the health sector have paved the way for diverse medical mobile expert systems. These systems are either individualised or used by medical expert (Ozdalga, Ozdalga & Ahuja, 2012). These portable application systems are designed to supplement the experts work in order to deliver a resource that will advance the results for private health monitoring and at the point of care (Aungst, 2013). There are existing medical expert system models and health calculators which include Breast Cancer Surveillance Consortium (BCSC) Risk Calculator (for breast cancer risk calculation), the Breast Cancer Risk Assessment Tool (the Gail model) often used by health care providers to estimate risk, MedCalc. These models did not explore detail risk factors for breast cancer growth, and detail fuzzy rules were not explored as well. Most of the mobile health calculators for breast cancer prognosis are not user friendly. They are not readily available for personal use and are majorly used by the medical professionals in the the health care sectors.
World Health Organisation (WHO) in 2012 described cancer as a leading cause global deaths. In, 2008 cancer accounted for about 13% (7.6 million) deaths (WHO, 2017). There are divergent views on the exact cause of breast cancer. Though, knowing an individual risk factors and preventing the growth of the malignant (breast cancer) could be a preferred approach to tackling this disease because most research works that have developed models for prognosis and diagnosis have not actually reduced the death rate (Global Cancer Facts & Figures, 2015). This is because reviewed literatures have shown that the existing systems focused on diagnosing/prognosing the survivability and recurrence of the disease. By the time patients report at the hospital for diagnosis, the tumour has grown to the metastatic stage where survival is almost impossible.
Majority of the models applied in medical field are naturally unclear. As a result of the unclear (fuzzy) nature of medical data and models as well as the relationships that exist in the models, fuzzy logic technique is suitable for medical applications. Fuzzy logic (an aspect of soft computing) proposes approaches of result production that have the capability of estimated representation of decisions. As a result of the difficulty in medical exercise, the old-style numerical study methods are not satisfactory and may not be suitable. The utmost causes of ambiguity are as follows:
- Incomplete data about an individual: either from patient or family members
- Often time, the patient’s state of health account is provided by the individual, or by the family member. These data to a large extent are subjective and ambiguous.
- The well being check-up: Often time, medical practitioners get impartial facts
- Laboratory test and prognosis results may also be subject to various mistakes.
- The delinquency of patient’s preceding health status check-up can also cause error in the test report.
- Symptoms might be faked or overstated more/fewer than they truly appear.
- Patients are likely to neglect some of the symptoms.
- Some symptoms might be indescribable by patients
Hence, fuzzy logic a soft computing methodology has the capability to reduce uncertainty in decision making in medical field.
1.2 Statement of the Problem
The most recurrent and second leading cause of death in women is breast cancer. The inadequacies of the existing methods, such as Mammography, Magnetic Resonance Imaging (MRI), Self-examination and others, account for the breast cancer high mortality. The shortcomings of the existing models include:
- Late discovery of the cancerous germs – these methods only detect breast cancer at the metastatic stage. (the tumour has grown and spread to other parts of the body);
- Existing models cause patients pains and related inconvenience which dissuade women from voluntary screening. Thus, most people do not report cases of breast cancer until it has got to the third stage and stack the odd of survival against the patient.
- Imprecise diagnosis because it involves several layers of uncertainty. These shortcomings make the traditional approaches inappropriate.
Thousands of people fall victim to breast cancer every year due to limitation of medical services and the inability to use the existing services effectively. Late presentation of cases at advanced stages when little or no benefit can be derived from any form of therapy is the hallmark of breast cancer among Nigerian women. The available breast cancer calculators are only focused on survivability and re-occurence and also not safe because individuals do not know where their personal data is being saved. To curtail the worsening incidence of breast cancer deaths, a Mobile-based Fuzzy Expert System (MFES) for breast cancer pre-growth prognosis that would obviate the inadequacies of the existing models, encourage voluntary personal screening and more importantly, detect the risk of developing breast cancer is designed. Pre growth prognosis of a disease like breast cancer is very crucial to a successful reduction of death rate caused by the disease. This research weaved its solution/prognosis intervention around a nature motivated method that is biologically inspired. This method would be able to detect the risk of early developments and proffered likely solutions thereby reducing the consequence of ignorance which may lead to death.
1.3 Objective of the Study
The general objective of this study work is to design and implement a Mobile-based Fuzzy Expert System (MFES) for breast cancer pre-growth prognosis. The fuzzy expert system would be capable of capturing ambiguous and imprecise information prevalent in breast cancer prognosis. The specific objectives are to:
- determine the range values for the Membership Function (Breast Cancer Risk factors) using experts rating for the indicators for fuzzification;
- formulate the membership functions using information in (1);
- design a MFES for breast cancer pre-growth prognosis and
- implement and carry out performance evaluation of the developed mobile based fuzzy expert system using in comparison existing fuzzy logic models.
In order to achieve the stated objectives, the following approaches were considered:
- Upper and lower values were determined from the values (facts) collected from the domain experts to determine the membership functions.
- Membership functions for all the risk factors were formulated, using the values in (1).
- The rules for all the risk factors were formulated.
- Java expert system shell (JESS) was used to develop the MFES, using the informations in (1), (2) and (3) and this runs on Android systems.
- The MFES performance evaluation was carried out using data from healthy people and those already diagnosed with the disease and also in comparison with existing fuzzy logic models
- Justification for the Study
The inadequacy of existing methods to identify breast cancer at the early stage and late presentation is the hallmark in breast cancer issue. Based on the nature of the disease (breast cancer), a nature inspired methodology became the best approach to handle the short comings of existing models. Further review of existing literatures on soft computing approaches, revealed that researchers anchored their solutions on survivability and reoccurence of the disease. These approaches did not in no way reduce the death issues associated with the disease. Hence, this reasearch work proposes and designed a MFES for breast cancer pre growth prognosis. This system (MFES) using detailed risk factors and judiciously formulated membership functions and fuzzy rules could be capabale to pick the smallest information that initiates the growth of the disease. Recommendations are made to individuals on appropriate action(s) to take by the system.
1.6 Significance of the Study
Despite the enomous research work on breast cancer, using fuzzy logic approaches death rate is still on the increase. These research works focus were majorly on survivabilty and recurrence of the disease which has not recorded reasonable decrease in the global death rate caused by the disease. Hence, there is the strong reason to tackle this contemporary issue in a different approach that would reduce/eliminate the life threatening disease. The findings of this study would benefit the global society considering the global death rate caused by the disease (Breast Cancer). Also considerating the great efficiency in the use of mobile system in recent time, this study is timely and relevant because breast cancer has become a serious contemporary issue. Global use of the designed MFES for breast cancer growth prognosis by females would reduce/eliminate this life threatening disease (breast cancer). The end users of the designed MFES ranges from teenagers to adults who have not been diagnosed of the disease in the global society. It is designed to run on widely used and existing personal mobile devices which runs on android operating system. This implies that it would be easily accessible at no extra cost.
1.7 Scope of the Study
Many research works have been carried out in breast cancer prognosis, but the incidence rate of breast cancer is still on the increase. The key issue here is on how to prevent the growth of breast cancer (tumour) and a user friendly system.
Having identified the gap (stated in chapter two) in other research work, this study covers pre growth prognosis of breast cancer using breast cancer risk factors to design a mobile based pre breast cancer growth prognosis – Fuzzy Expert System approach. The system is capable of capturing early enough ambiguous and imprecise information prevalent in the risk of developing breast cancer. The Mamdani fuzzy inference Model was adopted because this research work involves a number of fuzzy if-then rules, each of which describes the local behaviour of the mapping of each risk indicators (breast cancer risk factors). Java Expert System Shell (JESS) was used in the coding of the Mobile-based expert system which runs on Android personal devices. The Direct Rating method was used to elicit data (facts) from medical experts for the fuzzification stage of the model. Tumour sample data was not collected from the body of the individuals. Data was collected from both healthy people and those already diagnosed with the disease for the performance evaluation of the Mobile Expert System.
1.8 Operational Definition of Terms
Crisp Set: A collection of objects taken from the universe of objects.
Fuzzy: Refers to linguistic uncertainty.
Fuzzy Sets: Allow objects to have membership in more than one set.
Fuzzy Statement: Describes the grade of a fuzzy variable with an expression
Fuzzy Logic Rule: Uses membership functions as variables.
Statement: A sentence which unambiguously either holds true or holds false.
Membership Function: Defines a fuzzy set by mapping crisp values from its domain to the sets associated degree of membership.
Universe of Discourse: Range of all possible values, or concepts, applicable to a system variable.
1.9 Biblical implication of disease
Behind every health issue and every emotional or spiritual problem resides the “Spirit of Fear.” The Spirit of Fear is the Devil’s faith working in people by using lies to control them. And if we dwell on those lies long enough, we will begin to believe them, thus resulting in responding to them which can lead to all kinds of problems. We need to discover the root cause behind our problems. The things that we experience may be a manifestation of a root. But thank God, we have an advocate, Jesus Christ, who is our Saviour and the Truth that sets us free (Romans 7:24-25, 8:2). Many of the diseases are being used by God to bring to surface what is in our hearts. God does not give disease. The disease is the manifestation from a heart condition. Just as a fever is an indication that something is amiss in our bodies. These are warning signs. And we can either cooperate with God or deal with what was exposed. God already knows our heart; it’s no surprise what is there. We are the ones who are surprised by what is there. But as they come to the surface, don’t run, don’t hide in the bushes like Adam did Deal with it.(The Holy Bible, King James Version)