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。

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.

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