This research has used the H1N1 disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of H1N1 disease using Multilayer Perceptron. The experiment using WEKA machine learning tool produced the best parameter's values for the datasets. The General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) has been used throughout the study as a guideline. Prediction model for H1N1 disease using MLP has been generated and MLP has performs the good result where the value of accuracy for the H1N1 disease is 88.57%

Ling, Amy Mei Yin

English

Universiti Utara Malaysia: UUM eTheses

Prediction Model for H l N l  Disease A project submitted to Dean of Research and Postgraduate Studies Office in full Fulfillment of the requirement for the degree Master of Science (Intelligent System) Universiti Utara Malaysia BY Amy Ling Mei Yin I t I KOLEJ SASTERA D M  SAINS (College of Arts and Sciences) Universitf Utara Malaysia PERAKUAN KERJA KERTAS PROJEK (Ceryfrcate of h j e c t  Paper) Saya, yang bertandatangan, memperakukan bahawa (7, the undersigned, certifies that) calon untuk Ijazah (candidate for the degree ofl Msc. flntelligent Smteml telah mengemukakan kertas projek yang bertajuk (has presented his/her project of the following title) PREDICTION MODEL FOR HlAl DISEASE wperti yang tercatat di xnuka surat tajuk dan kulit kertas projek (as it appears on the title page andfmnt cover of project) bahawa kertas projek tersebut boleh ditorima dati segi bentuk serta kandungan dm meliputi bidang ilmu dengan memuaskan. (that this project is in acceptable form and content, and that a satisfactory knowledge of the field is covered by the project). Nama Penyelia (Name of Superuisor) : MISS ANIZA MOHAMED DIN Tamlatangan (Sgnafu re) (Date) : z + / 3 / ~ 0 1 1  , . &dww Nama Penilai (Name of Evaluator) : Tanciatangan (Signature) : w -/ Tariich (Date) : WAN Z A W  BIN WAN MUOA lAaww ~ - - r * y k c h n o k o ~  WMm PERMISSION TO USE In presenting this project in partial fulfillment of the requirements for a postgraduate degree from Universiti Utara Malaysia, I agree that the University Library may make it freely available for inspection. I further agree that permission for copying of this project in any manner, in whole or in part, for scholarly purpose may be granted by my supervisor(s) or, in their absence by the Dean of Postgraduate and Research. It is understood that any copying or publication or use of this project or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to  me and t o  Universiti Utara Malaysia for any scholarly use which may be made of any material from my project. Requests for permission t o  copy or t o  make other use of materials in this project, in whole or in part, should be addressed t o  Dean of Research and Postgraduate Studies College of Arts and Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Darul Aman Malaysia ABSTRAK Kajian ini mengunakan data HlNl daripada Hong Kong yang di kumpulkan daripada pesakit dari klinik (sektor persendirian dan swasta) di seluruh Hong Kong dengan influenza yang sama. Objektif kajian ini adalah untuk menbina model ramalan untuk penyakit HlNl dengan mengunakan Multilayer Perceptron. Eperiment ini mengunakan WEKA machine learning sebagai. perkakas untuk mencipta nilai parameter untuk data tersebut. General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) telah digunakan sebagai pengukur rujukan dalarn kajian ini. Model ramalan untuk HlNl  mengunakan MLP telah dihasilkan dan MLP menunjukkan keputusan prestasi yang baik dengan nilai ketepatan untuk penyakit HlNl  adalah 88.57%. Kata kunci: HlNl, Multilayer Perceptron, Nilai ketepatan ABSRACT This research has used the HlNl disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of HlNl  disease using Multilayer Perceptron. The experiment using WEKA machine learning tool produced the best parameter's values for the datasets. The General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) has been used throughout the study as a guideline. Prediction model for HlN 1 disease using MLP has been generated and MLP has perfoms the good result where the value of accuracy for the HlNl disease is 88.57%. Keywords: HINI disease, Multilayer Perceptron, Accuracy's values ACKNOWLEDGEMENTS First of all, I would like to express my gratitude and appreciation to my supervisor Miss Aniza for her help, guidance and encouragement. Without her encouragement and guidance, it will not be easy for me to reach till this extends in my report completion. Secondly, I would like to thanks my beloved fbture husband, Hong Kok Pan who always is ready to help and support me throughout my report. His full support remains the mainstay for me in overcoming all the difficulties in completing this project. Without his all angle support, it is impossible for me to finish up my project on time. TABLE OF CONTENTS PERMISION TO USE ABSTRACT (BAHASA MALAYSIA) ABSTRACT (ENGLISH) ACKNOWLEDGEMENT LIST OF TABLES LIST OF FIGURES CHAPTER ONE: INTRODUCTION 1.1 The Context of the Study 1.2 Statement of the Problem 1.3 Objectives of the Study 1.4 Significance of Study 1.5 Scope, Assumptions and Limitations of the Study 1.5.1 Scope 1.5.2 Assumptions of the Study 1.5.3 Limitations of the Study 1.6 Organization of the Report CHAPTER TWO: LITRERATURE REVIEW Page I1 I11 IV v VIII IX 2.1 Data Mining 2.2 Neural Network 2.3 Prediction in Medical 2.4 Influenza A (HIN1) CHAPTER THREE: METHODOLOGY 3.1 Introduction to WEKA Software Machine Learning Tools 3.2 Methodology 3.2.1 Awareness of Problem 3.2.2 Requirement Gathering 3.2.3 Rule Extraction 3.2.4 Evaluation CHAPTER FOUR: RESULT ANALYSIS 4.1 To determine the most suitable number of Hidden Units 4.2 To determine the most suitable Learning Rate 4.3 To determine the most suitable Momentum Rate 4.4 To determine the most suitable Number of Epoch 4.5 To determine the most suitable Percentage Split 4.6 The Network Architecture 4.7 Summary CHAPTER FIVE: CONCLUSIONS 5.1 Recommendation and Future Work REFERENCES C p. I b alm Ir Y Y, Isl I iL. 111 I) C 111 I List of table List Descriptions Table 3.2.3 Percentage of Splitting data Table 4 Starting parameters Table 4.1 (a) Result to determine the best number of hidden unit Table 4.1 (b) Result to determine the best Hidden Unit using various Weight Seed Table 4.2 Result to determine the best Learning Rate Table 4.3 (a) Result to determine the best Momentum Rate Tables 4.3 (b) Results of using Momentum 0.1,0.2 and 0.3 using various Weight Seeds Table 4.4 Result to determine the best Number of Epoch Table 4.5 Result to determine the best Split Percentage Table 4.7 Neural Network Model and the optimum parameters LIST OF FIGURE List Descriptions Figure 2.2 (a) A Biological Neuron Figure 2.2 (b) An Artificial Neuron Figure 3.1 Example of WEKA's Interface software Figure 3.2 General Methodology of Design Research (GMDR) and KDD Process (Fayyad et al., (1996)) Figure 3.2.3 (a) Original Data Figure 3.2.3.b (i) Layout of data imported to WEKA Figure 3.2.3.b (ii) Missing value before preprocessing data Figure 3.2.3.g Layout of changing parameter in WEKA Figure 4.1 Result to determine the best Hidden Unit using various Weight Seed Figure 4.3 Result to determine the best Momentum Rate using various Weight Seed Figure 4.6 Neural Network Architecture CHAPTER 1 INTRODUCTION 1.1 The Context of the Study In the spring of 2009, a newly identified flu virus called influenza A (or HlNl)  spread rapidly among people (Mabrouk & Marzouk, 2010). Based on the information from the Centers for Disease Control and Prevention (CDC), within a week, the virus spread worldwide to 30 countries by animal-to-human and human-to-human. According to the latest World Health Organization (WHO) statistics, there are more than 18,000 people died because of this virus since it was identified on April 2009. HlNl virus has spread to enough countries to be considered as a global pandemic. Influenza epidemics can seriously affect the health of all ages particularly children younger than 2 years old and adult age 65 or older. People especially with certain medical conditions such as liver, lung, chronic heart, kidney, blood or metabolic diseases or weakened immune systems are at higher risk of being contacted with this disease. Patients of HlNl disease suffer because this disease is still unknown. 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Prediction Model for H1N1 Disease

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Prediction Model for H1N1 Disease

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Universiti Utara Malaysia: UUM eTheses