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PROJECT TOPIC AND MATERIAL ON A WEB-BASED CLINICAL DECISION SUPPORT SYSTEM FOR THE MANAGEMENT OF DIABETES NEUROPATHY USING NAÏVE BAYES ALGORITHM

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  • Name: A WEB-BASED CLINICAL DECISION SUPPORT SYSTEM FOR THE MANAGEMENT OF DIABETES NEUROPATHY USING NAÏVE BAYES ALGORITHM
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ABSTRACT

The use of Artificial Intelligence in medical diagnosis is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, diabetes, hepatitis, lung diseases, etc. There is a growing interest in the use of computer-based Clinical Decision Support Systems (CDSSs) to reduce medical errors and to increase health care quality and efficiency. Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences.Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy.This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy.

Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump and Rule Induction) were adopted in this study and were evaluated to choose the most precise algorithm to be employed in the developed clinical decision support system. The evaluation was carried out on appropriate dataset that was obtained from Babcock University Teaching Hospital and the University of Port Harcourt Teaching Hospital. The following benchmarks were used in comparing the generated models: performance, accuracy level, precision, confusion matrices and the models building’s speed.

From the models comparison, the study showed that Naïve Bayes outperformed all other classifiers with accuracy being 60.50%. k-nearest neighbor, Decision Tree, Decision Stump and Rule induction perform well with the lowest accuracy for x- cross validation being 36.50%. Decision Tree falls behind in accuracy, while k-nearest neighbour and Decision Stump maintain accuracy at equilibrium 41.00%. Therefore, Naïve Bayes is adopted as optimal algorithm in the domain of this study. The rules generated from the optimal algorithm (Naïve Bayes) forms the back-end engine of the Clinical Decision Support System.The web-based clinical decision support system was then designed using Adobe Creative Suite 6 as its integrated development environment in which all the web language codes was executed, Php My Admin as the server side scripting language, and MySQL as the database server.

In conclusion, the automatic diagnosis of diabetes neuropathy is an important real-world medical problem. Detection of diabetes neuropathy in its early stages is a key for controlling and managing patients early before the disabling effect present. This system can be used to assist medical programs especially ingeographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. For further studies, researchers can improve on the proposed clinical decision support system by employing more than one efficient algorithm to develop a hybrid system.

Keywords: Diabetes, Neuropathy, Precision, Classification, Algorithm, Accuracy, Clinical.

Word Count: 422

 

 

TABLE OF CONTENTS

Title Page                                                                                                                    i

Certification                                                                                                                            ii

Dedication                                                                                                                              iii

Acknowledgements                                                                                                                iv

Abstract                                                                                                                                      v

Table of Contents                                                                                                                      vi

List of Tables                                                                                                                            vii

List of Figures                                                                                                                          viii

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.        Scope of the Study                                                                                                       5

1.6.        Significance of Study                                                                                                   5

1.7.      Organization of the Subsequent Chapters                                                                      5

CHAPTER TWO: REVIEW OF LITERATURE

2.0.        Introduction                                                                                                                  6

2.1.        Clinical Decision-Making                                                                                             7

2.1.1.     Clinical Decision Analysis (Problem Solving)                                                              7

2.1.2.     Structure of Clinical Decision Support Systems                                                         10

2.1.3.     Elements of CDSS Design and Function                                                                   10

2.1.4.     CDSS Knowledge Axes                                                                                             12

2.1.5.     CDSS Decision support Axes                                                                                     13

Content                                                                                                                                 Page

2.1.6.     CDSS Information Delivery Axes                                                                              14

2.1.7.     CDSS Model                                                                                                                          14

2.1.8.     CDSS Knowledge Representation                                                                             15

2.1.9.     Knowledge Engineer                                                                                                              16

2.1.10.   CDSS Knowledge Acquisition                                                                                   16

2.1.11.   CDSS User Interface                                                                                                              17

2.2.        Overview of Diabetes Neuropathy                                                                             18

2.2.1.     Causes of Diabetes Neuropathy                                                                                 18

2.2.2.     Symptoms of Diabetes Neuropathy                                                                            18

2.2.3.     Classification of Diabetes Neuropathy                                                                       19

2.2.4.     Diagnosis of Diabetes Neuropathy                                                                             19

2.2.5.     Early Detection of Diabetes Neuropathy                                                                   20

2.3.        Review of Closely Related Works                                                                             21

2.3.1.   A Novel Analysis of Diabetes Mellitus by Using Expert System

Based on Brain Derived Neurotropic Factor (BDNF) Levels                                      21

2.3.2.   Web Based Intelligent Decision Support System for Type 2

Diabetes Patients                                                                                                        21

2.3.3.   An Expert System for Diabetes Diagnosis using VP_Expert Shell                              21

2.3.4.   Web Based Medical Diagnosis System Using ANN-ARM

For Diabetes Mellitus                                                                                                    22

2.3.5.   Expert System for Diagnosis and treatment of Diabetes                                           22

2.3.6.   Design of a Diabetic Diagnosis Expert System Using Rough Sets                            22

2.3.7.   Mycin                                                                                                                            23

2.3.8.   Gideion                                                                                                                         23

Content                                                                                                                                 Page

2.3.9.   An Expert System for Diagnosing Diseases and Prescribing

Medication Using Visual Basic.Net (Autodoc)                                                            24

2.3.10. Computerized clinical decision support systems for chronic disease

management: A decision-maker-researcher partnership systematic review                  24

2.3.11. Clinical Decision Support System for Diabetes Disease Diagnosis using

fuzzy logic                                                                                                       24

2.4.      The limitations of related works reviewed                                                                   25

2.5.      Pattern Classification                                                                                                    25

2.5.1.   Approach to Pattern Classification                                                                               25

2.6.      Classification Algorithms                                                                                             27

2.6.1    Types of Classification Algorithms                                                                              28

2.7.      K-nearest Neighbors (KNN)                                                                                         28

2.8.      Decision Tree Algorithm (DTA)                                                                                   30

2.9.      Decision Stump (DS)                                                                                                  31

2.10.    Rule Induction (RI)                                                                                                      31

CHAPTER THREE: METHODOLOGY

3.0.      Introduction                                                                                                                  33

3.1.      Design a Web-based Clinical Decision Support System to Diagnose

and Manage Diabetes Neuropathy                                                                               33

3.2.      Research Tools                                                                                                              34

3.2.1.   Wamp Package                                                                                                             34

3.2.2.   Adobe Dreamweaver Creative Suite 6                                                                         34

3.2.3.   Software Development Life Cycle                                                                               35

3.2.4.   Spiral Model                                                                                                                 35

Content                                                                                                                                 Page

3.2.5.   History of Spiral Model                                                                                                36

3.3.      Clinical Decision Support System (CDSS) Modules                                                    38

3.3.1.   User Interface                                                                                                               38

3.3.2.   Medical Knowledge Base                                                                                             38

3.3.3.   Classification Algorithm Module                                                                                 39

3.3.4.   Inference Engine                                                                                                           39

3.3.5.   Database Design                                                                                                           40

3.3.6.   Tables                                                                                                                            40

3.3.7.   Use Case Diagram for the System                                                                                44

3.3.8.   Sequence Diagram                                                                                                        46

3.3.9.   Activity Diagram                                                                                                          47

3.4.      Data Acquisition                                                                                                           48

3.4.1.   Preparation of Acquired Dataset                                                                                  48

3.4.2.   Rapid Miner Software Version 6.2                                                                   48

3.5.      Classification of Dataset using the Naïve Bayes Theorem                                           49

3.6.      Naïve Bayes                                                                                                                  49

3.6.1.   How Naïve Bayes Theorem Works                                                                              49

3.7.      Post Research Benefits                                                                                                 51

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND

DISCUSSION OF FINDINGS

4.0.      Introduction                                                                                                                  52

Content                                                                                                                                 Page

4.1.      Dataset                                                                                                                                      52

4.1.1.   Dataset Acquisition                                                                                                      52

4.1.2.   Dataset Pre-processing                                                                                                  53

4.2.      Performance of the Classification Models                                                                    55

4.2.1.   Performance of Naïve Bayes Model                                                                             55

4.3.      Performance Evaluation with other Classification Algorithm                                      57

4.3.1.   Performance of K-nearest neighbor (K-NN)                                                                57

4.3.2.   Performance of Decision Tree                                                                                      58

4.3.3.   Performance of Decision Stump                                                                                   60

4.3.4.   Performance of Rule Induction                                                                                    61

4.3.5.   Comparison Summary Based on Classification Performance                           62

4.4.      Implementation of the Clinical Decision Support System for the

Management of Diabetes Neuropathy                                                                          63

4.4.1.   Medical Administrator                                                                                                  63

4.4.1.1. Login Page                                                                                                                   63

4.4.1.2. Administrator Dashboard Page                                                                                   64

4.4.1.3. Add Specialist Page                                                                                                     65

4.4.1.4. Add Symptoms Page                                                                                                   65

4.4.1.5. Add Medicare Page                                                                                                     66

4.4.1.6. Change Password Page                                                                                                66

4.4.2.   Medical User                                                                                                                 67

Content                                                                                                                                 Page

4.4.2.1. User Login Page                                                                                                          67

4.4.2.2. User Home Page                                                                                                          68

4.4.2.3. Personal Details                                                                                                           69

4.4.2.4. Change Password                                                                                                        70

4.5.      System Testing                                                                                                              71

4.5.1.   Software Component Testing                                                                                       71

CHAPTER FIVE: SUMMARY, CONCLUSION

AND RECOMMENDATIONS

5.1.      Summary                                                                                                                       72

5.2.      Conclusion                                                                                                                    72

5.3.      Recommendations                                                                                                        72

REFERENCES

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES

Table                                                                                                                                     Page

3.1.      Use Case description                                                                                                    45

4.1.      Dataset in MS Excel Format                                                                                         53

4.3.      Comparison of Classification models based on Accuracy                                            63

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF FIGURES

Figure                                                                                                                          Page

2.1.      General Model for CDSS                                                                                           15

2.2.      The Statistical and Structural Approaches to Pattern Classification

Applied to a Common Identification Problem                                                             27

2.3.      A Decision Tree Algorithm                                                                                           30

3.1.      The Spiral Model Diagram                                                                                            37

3.2.      Architecture of the Design CDSS for Diabetes Neuropathy                                       38

3.3.      Database of the Proposed CDSS for Diabetes Neuropathy                                         41

3.4a.    A Flow Diagram for the Proposed CDSS for Diabetes Neuropathy

from the User Point of View                                                                                        42

3.4b.    A Flow Diagram for the Proposed CDSS for Diabetes Neuropathy

from the Administrative Point of View                                                                        43

3.5.      Use Case Diagram for the CDSS for Diabetes Neuropathy                                         44

3.6.      Sequence Diagram of CDSS for the Proposed System                                                46

3.7.      Activity Diagram of CDSS for Diabetes Neuropathy                                                              47

4.1.      Architecture for the Data Mining Process                                                                    54

4.2.1.   Classification Process for Naïve Bayes Algorithm using the Rapidminer

6.2 Software                                                                                                                 56

4.2.2.   Classification Process for K-nearest neighbor Algorithm using the

Rapidminer 6.2 Software                                                                                              57

4.2.3.   Classification Process for Decision Tree Algorithm using the Rapidminer

6.2 Software                                                                                                                 59

4.2.4.   Classification Process for Decision Stump Algorithm using the Rapidminer

6.2 Software                                                                                                                 60

4.2.5.   Classification Process for Rule Induction Algorithm using the Rapidminer

6.2 Software                                                                                                                 62

4.4.1.1. Administrator Login Page                                                                                           64

Figure                                                                                                                                    Page

4.4.1.2. Administrator Dashboard Page                                                                                   64

4.4.1.3. Add Specialist Page                                                                                                     65

4.4.1.4. Add Symptoms Page                                                                                                   65

4.4.1.5. Add Medicare Page                                                                                                     66

4.4.1.6. Change Password Page                                                                                                67

4.4.2.1a. User Login Page                                                                                                         68

4.4.2.1b. New User Registration Page                                                                                      68

4.4.2.2. User Home Page                                                                                                          69

4.4.2.3a. Personal Detail Page                                                                                                  69

4.4.2.3b. Edit Personal Detail Page                                                                                          70

4.4.2.4. Change Password Page                                                                                                70

CHAPTER ONE

INTRODUCTION

1.1.Background to the Study

The world is fast evolving and in order to cope with the insatiable demand of the human race for the kind of living that can be described as top-notch in which people have all they need at their beck and call, there is the need to develop intelligent decision making applications that will drive systems or devices to carry out tasks that require human intelligence. This concept is known as Artificial Intelligence (AI).In science and technology, the desire for improvement is a constant subject which triggersadvancements.Technology has changed civilization in many different ways. Humans have always been on a path of progression through the help of technology, the twentieth and twenty-first centuries have seen a number of advancements that revolutionized the way people work, live and play.

Artificial Intelligence (AI) is the area of Computer Science focusing on creating expert machines that can engage on behaviours that humans consider intelligent. Artificial Intelligence is the branch of Computer Science that is concerned with the design and development of the intelligent systems. Recent advances in the field of Artificial Intelligence have led to the emergence of expert systems and computational tools; designed to capture and make available the knowledge of experts in a field.Expert system is an area of Artificial Intelligence that emulates the decision-making ability of a human expert (Jackson, 1998). Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code.The use of computer technology in areas of diagnosis, treatment of illnesses and patient pursuit has highly increased. Though, the fields in which computers are being used have very high complexity and uncertainty; the uses of intelligent systems such as fuzzy logic, artificial neural network and genetic algorithm have been developed (Jimoh et al, 2014).

A Clinical Decision Support System (CDSS) is an active knowledge system, where two or more items of patient data are used to generate case-specific recommendation(s) (Chen et al, 2002). This implies that a CDSS is a Decision Support System (DSS) that uses knowledge management to achieve clinical advice for patient care based on some number of items of patient data. This helps to ease the job of healthcare practitioners, especially in areas where the number of patients is overwhelming. Clinical decision support system (CDSS) provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. A CDSS can also be seen as an application that analyses data to help healthcare providers make clinical decisions (Rouse, 2014).

Diabetes mellitus is a group of metabolic diseases characterized by elevated blood glucose levels (Hyperglycemia) resulting from defects in insulin secretion, insulin action or both. Insulin is a hormone manufactured by the beta cells of the pancreas, which is required to utilize glucose from digested food as an energy source. It is a hormone made by the pancreas that allowsthe body to use sugar (glucose) from carbohydrates in the food that you eat for energy or to store glucose for future use. Insulin helps keeps blood sugar level from getting too high (hyperglycemia) or too low (hypoglycemia) (Hess, 2014). Chronic hyperglycemia is associated with microvascular and macro vascular complications that can lead to visual impairment, blindness, kidney disease, nerve damage, amputations, heart disease, and stroke (Harris, 2007). Symptoms of diabetes include polydipsia (increased thirst), polyuria (increased urine volume), blurring of vision, recurrent infections, and unexplained weight loss. In severe cases, drowsiness, coma and high levels of glycosuria are usually present.

There are three major types of diabetestype 1 diabetes, type 2 diabetes, and gestational diabetes. All types of diabetes mellitus have something in common. Normally, the body breaks down sugars and carbohydrates into a special sugar called glucose. Glucose fuels the cells in the body, but the cells need insulin, a hormone in the bloodstream in order to take in the glucose and use it for energy. With diabetes mellitus, either the body doesn’t make enough insulin or cannot use the insulin it does produce, or a combination of both. Since the cells can’t take in the glucose, it builds up in the blood. High levels of blood glucose can damage the tiny blood vessels in the kidneys, heart, eyes, or nervous system. Diabetes if left untreated, can eventually cause heart disease, stroke, kidney disease, blindness, and nerve damage (Diabetic Neuropathy) a type of nerve damaging disorders associated with diabetes mellitus (Papadakis, et al, 2014).

Diabetic neuropathy is the most common complication of diabetes mellitus, affecting as many as 50 percent of patients with type 1 and type 2 diabetes mellitus.(Khardori, 2014). About half of all people with diabetes have some form of nerve damage and up to 26 percent of people with type 2 diabetes have evidence of nerve damage at the time that diabetes is diagnosed(Davies et al, 2006). It is more common in those who have had the disease for a number of years and this can lead to different kinds of problems like sensory loss and damage to the limbs. It can also cause impotence in diabetic men. Major risk factors of this condition are the level and duration of elevated blood glucose.Getting diabetes under better control also may help limit the amount of damage caused by neuropathy once it’s developed. The best way to prevent or stop the progression of diabetic neuropathy is to keep diabetes under control. Nerve damage or diabetic neuropathy resulting from chronically high blood glucose can be one of the most frustrating and debilitating complications of diabetes because of the pain, discomfort and disability it can cause, and because available treatments are not uniformly successful. Although physicians have found some medications and other treatments that help ease these symptoms in some people, prevention continues to be the key.  (Joslin, 2015). Poorly managed diabetes can lead to a host of long-term complications among these are heart attacks, strokes, blindness, kidney failure, and blood vessel disease that may require an amputation, nerve damage, and impotence in men. If the blood glucose is kept close to normal as possible, it can help in reducing the risk of developing some of these complications by 50 percent or more.

Computer based methods are increasingly used to improve the quality of medical services. The use of computer technology in areas of diagnosis, treatment of illnesses and patient pursuit has highly increased. Though, the fields in which computers are being used have very high complexity and uncertainty; the uses of intelligent systems such as fuzzy logic, artificial neural network and genetic algorithm have been developed (Jimoh et al, 2014). Artificial Intelligence (AI) is the area of Computer Science focusing on creating expert machines that can engage on behaviours that humans consider intelligent. Artificial Intelligence is concerned with the design and development of the intelligent systems. Recent advances in the field of Artificial Intelligence have led to the emergence of expert systems and computational tools; designed to capture and make available the knowledge of experts in a field.  Hence, this work focuses on the design and implementation of a web-based clinical decision support system for the management of early diabetes neuropathy.

1.2.      Statement of the Problem

Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences. Over 135 million people worldwide suffer from diabetes, with 25% developing podiatric problems related to the disease, such as diabetic neuropathy. This neuropathy often causes severe pain and can be incapacitating.Globally, rates of diabetes neuropathy were 15.1 million in 2000 (Skaff et al, 2003). According to Nau et al (2007), 7.8% of 23.6 million people of USA population were recorded for having type 2 diabetes that resulted to diabetes neuropathy. (Nau et al, 2007).The World Health Organization (WHO) estimates the number of diabetes patients will reach 300 million by 2025(medoc, 2014). Four to five percent of health budgets are spent on diabetes-related illnesses, such as the management of diabetic neuropathy and its consequences.

According to the World Health Organization, there is one diabetes specialist available for 10,000 Nigerians (WHO, 2012). Methods are needed to quantitatively evaluate the integrity of both small and large-caliber sensory nerve fibers in order to detect and manage this condition early in its progression. Since diabetic neuropathy is an irreversible condition, early detection is a key factor for controlling and managing patients early before the disabling effects present.Due to this shortage of specialists, there is a need for a Clinical Decision Support System that willdiagnose and manage diabetes neuropathy. Existing Clinical Decision Support Systems make use of patients symptoms, medical history, physical exam and the blood sugar level to diagnose patients and come up with a valid result as regards whether such person(s) have diabetes neuropathy or not. However, very little research have been carried out using the genetic predisposition, loss of nerve function and plasma insulin level of the individual in question to predict his or her susceptibility to diabetes, thus making this aspect presently pose itself as a gray area. This research consequently proposes a web-based adaptive clinical decision support system to diagnose and manage diabetes neuropathy based on the genetic predisposition, loss of nerve function and plasma insulin level of the person(s) in question.

1.3.    Objective of the Study

The main objective is to develop a Web-based Clinical Decision Support System to diagnose and manage Diabetes Neuropathy using Naïve Bayes Algorithm.

The specific objectives are to:

  1. design a web-based clinical decision support system to diagnose and manage diabetes neuropathy;
  2. classify diabetes neuropathy using Naïve Bayes Theorem and
  3. carryout performance evaluation between Naïve Bayes theorem and four well known classification algorithms (K-nearest neighbor (KNN), Decision Tree (DT), Decision Stump (DS), and Rule Induction (RI) based on appropriate dataset using the Rapidminer 6.2 software.

1.4.Methodology

To achieve the set objective, the following procedures was adopted.

  1. Design and implement a web-based clinical decision support system to diagnose and manage diabetes neuropathy.

The web-based CDSS interface was developed using:

  1. Apache serves as the server used to execute the web code created with HTML and PHP.
  2. MySQL as its database management system
  • PHP Programming Language as its server side scripting language and was used to connect to the database.
  1. Cascading Style Sheet (CSS) was used to make all the designs of the CDSS
  2. Adobe Dreamweaver CS6 was the Integrated Development Environment (IDE) used in coding.
  3. Critical review on existing works was carried out and then dataset was acquired from the University of Port Harcourt Teaching Hospital (UPTH) and Babcock University Teaching Hospital (BUTH). The pre-processed dataset was classified using Naïve Bayes Theorem.
  4. Performance evaluation was carried out between Naïve Bayes theorem and four well known classification algorithms (k-nearest neighbor (KNN), Decision Tree (DT), Decision Stump (DS) and Rule Induction (RI) based on the acquired dataset using the Rapidminer 6.2 software to pick the optimal algorithm that will serve as the back-end engine to the CDSS.

1.5.      Scope of the Study

This dissertation intends to make contributions in terms of generating patient-specific Clinical Pathways (CPs) for diabetes that targets the knowledge needs and clinical duties of General Practitioners (GPs), especially those working in Nigeria and sub-Saharan Africa as a whole. The Clinical Pathways was based on existing Clinical Practice Guidelines (CPGs),it was made to incorporate expert medical knowledge of specialists that treat diabetes who are domicile in Nigeria.

1.6.      Significance of the Study

This research serves as the platform upon which an intelligent Clinical Decision Support Systemwas built and operationalized for the diagnosis and management of early stages of diabetes neuropathy. It makes it possible for Medical Practitioners (GPs) who have little idea in the area of diabetes to have insights into the diagnosis and management including patient-specific care plans for patients with diabetes neuropathy.This study could support academic development and also contribute to the improvement of education in architecture not only in this faculty but also in other discipline.It could also help improve the degree of awareness to Nigerians and also rendering useful preventive measures to anyone who seek for it. The uniqueness of the this work is that, it does not only limit itself to diagnosis of the disease, but also renders useful management solutions, prescriptions and preventive measures to the user.

1.7.Organization of the Subsequent Chapters

Chapter One: gives the introduction or background to the study which includes the pertinent details and statistics about diabetes.

Chapter Two: deals with the literature review which includes the basic terms and concepts upon which this research work is hinged also the related literature that were thoroughly perused while doing this work.

Chapter Three: handles the research methodology which specifies how precisely we intend to successfully achieve the objectives of this study including the design tools that will be employed.

Chapter Four:deals with the analysis of the proposed work and its juxtaposition with other existing works as well as the results obtained from running the needed experiments.

Chapter Five: deals with the conclusion, recommendation and the suggestion for further research.

 

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