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Alzheimer's dementia (AD) affects memory, language, and cognition which worsens over time. It is critical to develop a reliable early detection method before permanent brain atrophy and cognitive impairment. This study uses clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer's patients. This audio transcript data is taken from DementiaBank which is the largest public dataset of AD transcripts. The study aims to show how transfer learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer's disease. To enhance the prediction performance for Alzheimer's disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm is proposed. The proposed model is compared using two feature sets: first set consists of the initial feature set and the second set contains hybrid feature set that has been extracted using the suggested HSI-LFS method. The BERT embedding with HSI-LFS outperformed the conventional feature set providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model excelled state-of-the-art models achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. A possible criterion for the early identification of Alzheimer's dementia is to identify the difference between positive and negative linguistic and cognitive abilities of the patients. This study involves the use of Convolutional Neural Network (CNN), designed a hybrid model with CNN & Bidirectional Long-Short Term Memory (Bidirectional LSTM), and proposed a Stacked Deep Dense Neural Network (SDDNN) model for text classification and prediction of Alzheimer's dementia. These models were trained end-to-end using DementiaBank clinical transcript dataset. The transcripts consisted of recorded interviews of Alzheimer's patients with clinical experts. The models were investigated under two settings: Randomly initialized and Glove embedding. Further, hyperparameter optimization was accomplished using GridSearch, which yielded optimal parameters for the design of suitable learning models for most accurate predictions. Other parameters were computed and compared based on AUC, accuracy, specificity, precision, F1 score, and recall. To ensure performance generalization, the classification accuracy was tested using 10-fold cross-validation approach. The performance and classification accuracy of the proposed model was significantly improved to 93.31% when applied with Glove embedding and hyperparameter tuning. This research work will considerably help the clinical experts in early detection and diagnosis of AD.

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