失智症是一種神經退化性疾病或血管疾病，其特徵在於精神功能下降，包括異常活動，行為和認知的症狀表現組合。阿茲海默症是最常見的失智症類型，約有60％至70％的失智症病例是阿茲海默症。此論文提出在磁核照影像資料中提取以數學模型量化的生物特徵來辨識，且更進一步地結合神經心理資料，從不同的角度來辨識阿茲海默症。所提出的演算法將具有海馬迴子區標示的磁核照影像，建模成表面模型後以萃取體積、表面積和曲率之重要的特徵，並透過兩種不同的特徵選取方式，將所萃取的特徵對其中具有統計上的顯著差異或是經由隨機森林的特徵選取來篩選。這些磁核照影像特徵將與神經心理資料串聯輸入於多層神經感測器來分類。分類器被設計於執行分類任務，分類任務為辨識輕度知能障礙是否轉變為阿茲海默症。本論文的主要貢獻為1.以更大的資料庫驗證前人實驗的正確性。論文額外從阿茲海默症腦神經影像計畫(ADNI)的數據庫中擷取178位受試者，相較於先前研究的62位受試者，共240位，以更大受試者的資料庫來驗證實驗的正確性。2.以具邏輯地特徵選取方式篩選特徵，並將其導入神經網路模型架構中，得到79.95%分類準確度。3.結合腦神經醫學影像特徵及神經心理資料，得到86.94%分類準確度，驗證兩種腦神經資料一起使用的分類器強度大於個別使用。我們期望將以機器學習方法所萃取到的特徵與醫生分享，並更進一步設計實驗驗證所萃取之生物特徵的正確性。

Dementia is neurodegenerative or vascular disorder which is characterized by declining mental function including a combination of symptoms for abnormal activity, behavior and cognitive. Alzheimer’s disease (AD) is the most common type of dementia and there are around 60 to 70 percent of dementia cases which is caused by AD. This thesis is dedicated to extract biomarkers quantified by mathematical model from structural Magnetic Resonance Imaging (MRI), and further combines neuropsychological data to identify AD from different aspects. The proposed algorithm will build the surface model from labeled structural MRI, and extract volume, surface area and curvature from different hippocampus sub-regions. And through two different feature selection methods, the extracted features are statistically significantly different or features are selected through random forest. These neuroimaging features will be cascaded with neuropsychological data, and be fed into multi-layer perceptron (MLP) to classify. The classifier is designed to execute classification task. The classification task is to identify whether Mild Cognitive Impairment (MCI) is converted to AD. The main contribution of this paper: 1. We test the model with enlargement dataset to know the impact with different sizes of dataset. We collect 178 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, compared to the 62 subjects in previous study, total 240 subjects with a larger database to verify the experiment. 2. The features were selected by logical feature selection method which imported into neural network architecture, and we achieved 79.95% classification accuracy. 3. Combining neuroimaging feature and neuropsychological data, we achieved 86.94% classification accuracy. We also verified that the model used two neurological data is stronger than the models with either neuroimaging or neuropsychological data. We expect to share the observation presented by the machine learning method with the doctor and further design the experiment to verify the correctness of the extracted biomarkers.

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