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系統識別號 U0026-2806201810412000
論文名稱(中文) 多層感知器和K-平均分群法用於阿茲海默症之神經心理資料分析
論文名稱(英文) Analysis of Neuropsychological Data for Alzheimer’s Disease via Multilayer Perceptron and K-means clustering
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) 李政憲
研究生(英文) Zheng-Xian Lee
學號 N26041343
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  K平均演算法  群集分析法  阿茲海默症  神經心理學評估 
英文關鍵字 Multilayer Perceptron  K-means Clustering  Cluster Analysis  Alzheimer’s disease  Neuropsychological Assessment 
學科別分類
中文摘要 阿茲海默症為一種在失智症中佔據六至八成比例的疾病。因為病發過程緩慢,加上隨著時間流逝人腦持續不斷惡化以及在病症初期,診斷結果常被誤視作正常老化現象,均是當前不易診斷的因素之一。因此本論文應用兩種類型的機器學習演算法─K平均演算法以及多層感知器,分析神經心理學資料和人口統計資料。因兩者演算法架構不同,使用芮氏指標來衡量K平均分群的聚類性能,而以靈敏度、特意度和準確度用來衡量多層感知器方的性能。藉由觀察人口因子與簡易心智量表組合的表現和其阿茲海默症之關係,選擇較好的模組。因此解此幫助醫師診斷受測者是否會有阿茲海默症,以及減少誤診情況。
英文摘要 Alzheimer's disease is a cause of dementia that accounts for 60-80% of the dementia cases. Because of the slow progression of symptoms, the human brain degenerates gradually over time. Furthermore, at the early stage of the illness, the diagnosis results are often attributed to aging of the individual. This is one of the factors that makes diagnosis of this disease difficult. Therefore, this thesis applied two types of machine learning algorithms—k-mean clustering and multilayer perceptron—to analyze neuropsychological data and demographic data. Because the two models have different mechanisms, the Rand index is used to measure the clustering performance of K-means, while sensitivity, specificity, and accuracy are used to measure performance of the multilayer perceptron algorithm. The relationship between Alzheimer’s disease and different combinations of demographic factors with MMSE was observed to choose the better model. Thus, helping to diagnose individuals with Alzheimer's disease and reducing instances of misdiagnosis.
論文目次 Table of Contents
摘 要 iii
Abstract iv
誌 謝 vi
Table of Contents viii
List of Tables x
List of Figures xiii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Alzheimer’s disease 2
1.3 Neuropsychological data 3
1.3.1 Mini-Mental State Examination 4
1.4 Motivation 5
1.5 Organization of this thesis 6
Chapter 2 Surveys of Related Works in the Literature 7
2.1 Clustering 7
2.1.1 K-means Clustering 8
2.1.2 Hierarchical Clustering 10
2.2 Classification 13
2.2.1 Multilayer Perceptron 13
2.2.2 Support Vector Machine 14
2.2.3 Decision Tree 16
Chapter 3 Methodology 18
3.1 Observation of Neuropsychological and Demographic data 18
3.2 Training a K-means clustering model 19
3.2.1 Set number of cluster 20
3.2.2 Choosing initial points of cluster 21
3.2.3 Utilizing the distance 23
3.2.3.1 Euclidean distance 24
3.2.3.2 Mahalanobis distance 25
3.2.4 Rand Index 27
3.3 Training the Multilayer Perceptron Classifier 29
3.3.1 Setting architecture of Multilayer Perceptron 29
3.3.2 Feedforward 31
3.3.3 Backpropagation 32
3.3.4 Activation Function 35
3.3.5 Min-Max Normalized Method 37
Chapter 4 Experimental Results and Discussion 39
4.1 Data Specification 39
4.2 Observation of Data 40
4.3 Experimental Design and Results 42
4.4 Comparison and Discussion 55
Chapter 5 Conclusion and Future Work 59
5.1 Conclusion 59
5.2 Future Work 60
Acknowledgments 62
References 63
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