| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Alzheimer''s disease has become one of the biggest challenges in the healthcare system worldwide. Researches have shown that Alzheimer’s disease is the sixth leading cause of death in the United States and even the fifth leading cause among people aged 65 and older. Therefore, a screening system that can help the doctor to diagnose Alzheimer’s disease is demanded. In this thesis, we proposed a screening system based on the transcripts of speeches spoken by subjects undertaking a neuropsychology test. While most of the related studies have utilized extracted syntactic and semantic features and relied on a feature selection process, the proposed system used word vectors as the representation of a spoken speech, and Recurrent Neural Networks together with attention mechanism as the classifier. Using ten times 10-fold cross validation on an open dataset with 242 speeches samples spoken by healthy controls and 257 samples spoken by subjects with Alzheimer''s disease in the USA, a mean accuracy of 0.83 is achieved in our work. And the classification of 43 healthy subjects and 43 subjects with Mild Cognitive Impairment, the model can still achieve 0.71 of accuracy. On the other hand, validate on 40 Taiwanese subjects with AD and 40 healthy Taiwanese subjects, and 30 Taiwanese subjects with MCI and 30 healthy Taiwanese subjects, accuracy of 0.89 and accuracy of 0.8 can be achieved, respectively.
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