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系統識別號 U0026-0105201815240400
論文名稱(中文) 多層感知器與支持向量機用於分類阿茲海默症、輕度知能障礙與正常人群基於神經心理資料
論文名稱(英文) Classification of Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Aging based on Neuropsychological Data via Multilayer Perceptron and Support Vector Machine
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 106
學期 1
出版年 107
研究生(中文) 黃柏瑋
研究生(英文) Po-Wei Huang
學號 N26041466
學位類別 碩士
語文別 英文
論文頁數 67頁
口試委員 指導教授-李國君
口試委員-鄭國順
口試委員-林哲偉
中文關鍵字 多層感知器  支持向量機  阿茲海默症  輕度知能障礙  神經心理資料 
英文關鍵字 Multilayer Perceptron  Support Vector Machine  Alzheimer’s disease  Mild Cognitive Impairment  Neuropsychological data 
學科別分類
中文摘要 失智症為心智能力下降,嚴重程度足以影響到日常生活的總稱。阿茲海默症是最常見的失智症類型,佔了整體失智症病例的六至八成。由於阿茲海默症為一種神經退化性疾病,患者在罹患此症的初期,容易被判斷為只是正常老化的現象。而不同的醫生對同一個患者的診斷結果也不盡相同。本論文藉由機器學習演算法並基於神經心理資料,偵測受試者並將其歸類為阿茲海默症、輕度知能障礙,或是正常老化,三種類別。透過自動的計算機輔助診斷,使得醫生在判斷此症時有更多的參考依據。我們從阿茲海默症腦神經影像計畫(ADNI)的數據庫中挑選了399位病患的簡易智能狀態測驗(MMSE)資料進行實驗。本論文的主要貢獻在於找出MMSE中兩個最具代表性的細項,即定向力與短期回憶,它們偵測阿茲海默症的能力與整個MMSE量表相同。目標是希望透過快速的診斷令醫生與患者從中獲益,另一方面也使得醫療資源可以被更有效率的運用。
英文摘要 Dementia is a general term for a decline in mental ability, which is severe enough to interfere with patient’s daily life. Alzheimer's disease (AD) is the most common type of dementia, it accounts for 60 to 80 percent of dementia cases. Because AD is one kind of neurodegenerative diseases, it is easily considered as normal aging processes when the subject is actually in the early stages of AD, and different doctors may have different opinions on the status of the same subject. This thesis proposed a machine learning algorithm based on neuropsychological data to classify subjects into Alzheimer’s disease, mild cognitive impairment and normal aging. Through the automatic computer-aided diagnosis, doctors may have diagnostic results to refer to before they make any decisions. We acquired neuropsychological data from 399 participants with clinical diagnosis information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and we focus on Mini-Mental State Examination (MMSE) which is the most extensively used psychometric examination in the clinical practice. The major contribution of this thesis is that we found two valuable features from MMSE, orientation and recall, which have the same ability as the entire MMSE, to detect the Alzheimer’s disease. The goal is to benefit doctors and patients with faster detection of the disease, which can in reverse to have the medical resources being used more efficiently.
論文目次 摘 要 iii
Abstract v
誌謝 vii
Table of Contents ix
List of Tables xii
List of Figures xiv
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Alzheimer’s Disease 2
1.3 Mild Cognitive Impairment 3
1.4 Neuropsychological Data 5
1.4.1 Mini-Mental State Examination 6
1.5 Motivation 7
1.6 Organization of this Thesis 8
Chapter 2 Surveys of Related Works in the Literatures 9
2.1 Classification 9
2.1.1 Multilayer Perceptron 9
2.1.2 Support Vector Machine 11
2.1.3 Random Forests 12
2.2 Clustering 14
2.2.1 K-means Clustering 15
2.2.2 Hierarchical Clustering 17
Chapter 3 Methodology 20
3.1 Observation of Neuropsychological Data 20
3.2 Training Process of Supervised Classifiers 21
3.2.1 Multilayer Perceptron 21
3.2.1.1 Architecture 22
3.2.1.2 Initialization 23
3.2.1.3 Forward Propagation 23
3.2.1.4 Back Propagation 24
3.2.1.5 Activation Function 26
3.2.2 Support Vector Machine 28
3.2.2.1 The Concept of SVM Algorithm 31
3.2.2.2 Support Hyperplane and Objective Function 31
3.2.2.3 Lagrange Multiplier Method 33
Chapter 4 Experimental Results and Discussion 36
4.1 Data Specification 36
4.2 Data Analysis 37
4.3 Experimental Design 39
4.4 Experimental Results 42
4.4.1 Classification of AD and Normal Aging 42
4.4.2 Classification of AD, MCI, and Normal Aging 49
4.5 Comparison and Discussion 55
Chapter 5 Conclusion and Future Work 58
5.1 Conclusion 58
5.2 Future Work 58
Acknowledgments 60
References 61
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