Enhancing Alzheimer's Diagnosis with Pre-trained Embeddings and Attention Mechanisms

Alzheimer's disease (AD) is a progressive brain disorder that leads to a decline in cognitive and functional abilities. It is one of the most common causes of dementia, with far-reaching consequences not only for patients and caregivers but also globally. Although memory loss is often regarded as the hallmark of Alzheimer's disease, language impairment can also manifest in the early stages. Early diagnosis is crucial, as therapeutics can delay the progression of the disease and provide those diagnosed with valuable time. In this work, Natural Language Processing (NLP) approaches were utilized to classify dementia patients’ spontaneous speech from the DementiaBank dataset to predict dementia. Additionally, Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models were employed to differentiate between dementia patients and control subjects. It was found that using pre-trained GloVe embeddings significantly improved accuracy compared to random embeddings when applying the mentioned models. The transfer learning technique for text classification yielded more promising results than training the entire dataset on a large number of neural network model parameters. Performance indicators such as precision, recall, F1 score, and specificity were evaluated. Furthermore, the incorporation of the attention mechanism into the model, along with hyperparameter optimization of the CNN-LSTM model, resulted in excellent accuracy.