臺灣博碩士論文加值系統

失智症是一種逐漸惡化的神經退行性疾病，嚴重影響腦部功能，導致記憶力下降、認知能力衰退和行為改變。隨著全球人口老齡化問題日益嚴重，失智症已成為一個重大的公共衛生挑戰。《精神障礙診斷與統計手冊》（DSM）將失智症重新歸類為一種神經認知障礙(Neurocognitive Disorder)，這標誌著對該疾病更全面理解的轉變。與其以往主要將失智症歸類為認知障礙不同，DSM-5在知道失智症是一種涉及多種神經症狀和缺陷的複雜疾病。這一重新分類強調了失智症的神經生物學基礎，並鼓勵臨床醫生在診斷和治療時考慮認知神經因素。利用機器學習技術，我們可以從大型醫學數據集中識別相關特徵，從而提高失智症的預測和診斷能力。本文目的在利用支援向量機（Support Vector Machine，SVM）和其他機器學習方法來分析潛在相關特徵，以預測失智症。我們的目標是改善早期失智症的識別，促進及時介入和治療，最終提高患者的生活質量和預後。

Dementia, a progressive neurodegenerative disease, significantly impacts brain function, leading to memory loss, cognitive decline, and behavioral changes. With the global aging population, dementia has emerged as a major public health challenge. The reclassification of dementia by the Diagnostic and Statistical Manual of Mental Disorders (DSM) as a neurocognitive disorder signifies a shift towards a more comprehensive understanding of the condition. Unlike its previous classification primarily as a cognitive disorder, DSM-5 recognizes dementia as a complex disorder involving not only cognitive decline but also a wide range of neurological symptoms and deficits. This reclassification emphasizes the underlying neurobiological basis of dementia and encourages clinicians to consider both cognitive and neurological factors when diagnosing and managing the condition. Leveraging machine learning (ML) techniques presents an opportunity to enhance dementia prediction and diagnosis by identifying relevant features from large medical datasets. This paper aims to utilize Support Vector Machine (SVM) and other ML methods to predict dementia by analyzing potentially relevant features. The objective is to improve early dementia identification, facilitate timely intervention and treatment, and ultimately enhance patient quality of life and prognosis.

摘要... i
Abstract...ii
誌謝... iii
目錄... iv
表目錄...vi
圖目錄...vii
第一章緒論... 1
1.1研究動機... 1
1.2研究目的... 1
1.3研究限制... 2
1.4研究流程... 2
第二章文獻探討...4
2.1支援向量機... 4
2.2支援向量機領域... 4
2.3支援向量機應用... 4
2.4支援向量機等醫療保健中的應用與挑戰... 5
2.5支援向量機在失智症研究與診斷中的應用... 5
2.6使用支援向量機進行老年失智預測的方法... 5
第三章研究方法...7
3.1支援向量機... 7
3.2核心與參數... 7
3.3研究工具... 12
3.4研究數據... 12
3.5資料預處理... 14
3.6模型分類... 16
3.7性能指標... 16
第四章研究結果...20
4.1神經影像與認知研究... 20
4.2特徵與評估... 21
4.3模型分類視覺化... 44
第五章結論... 46
5.1模型性能概述...46
5.2 MRI Metric的有無影響...46
5.3實際影響... 46
5.4未來研究建議...46
參考文獻...48
1.1英文部分... 48
1.2中文部分... 49
Extended Abstract...50

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