阿茲海默症為一種失智症，相較於其他類別的失智症，阿茲海默症占了六到七成，是一種發病進程緩慢、隨著時間不斷惡化的神經退化性疾病。初期症狀常被誤認為是正常的老化狀況，因而患者的確診並不容易，本論文藉由使用機器學習演算法，透過分析神經心理資料以及人口統計資料，讓分類器來幫助醫學上偵測患者有無阿茲海默症，目的是減少社會財政的消耗，並增加醫療資源的效率。所使用的機器學習演算法為多層感知器和樸素貝葉斯分類器，以分類器的靈敏度、特異度、準確度指標，比較不同項目與阿茲海默症之間的關係，讓往後的長期追蹤治療能夠更有依據地針對不同項目做檢查。
關鍵字: 多層感知器、樸素貝葉斯分類器、阿茲海默症、神經心理資料

Alzheimer’s disease is a type of dementia. It is the cause of 60% to 70% of cases of dementia. Since it is a chronic neurodegenerative disease and its initial symptoms are often mistaken for normal ageing so that it is difficult to have a definite diagnosis. This thesis proposed a machine learning algorithm through the analysis of neuropsychological data and demographic data to make classifier help detect whether subjects suffer from Alzheimer’s disease or not in medicine. The goal is to reduce the financially cost and increase the efficiency of medical resources. This thesis uses Multilayer Perceptron and Naïve Bayes Classifier with three indicators, sensitivity, specificity, and accuracy, to compare the relationship between Alzheimer’s disease and different items and make the examination of longitudinal treatment period aim at more predictive items.
Keyword: Multilayer Perceptron, Naive Bayes Classifier, Alzheimer’s disease, Neuropsychological data

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