A Novel Screening System for Alzheimer’s Disease Based on Speech Transcripts Using Neural Network

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. Moreover, the number of patients is escalating rapidly in recent years, which also increases the burden on the healthcare system. Therefore, a screening system that can help the doctor to diagnose Alzheimer’s disease is demanded. In this paper, 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 Network 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, a mean accuracy of 0.835 is achieved in our work, which is outperforming the current state-of-the-art while requiring less effort.