Abstract
Alzheimer's dementia (AD) affects memory, language, and cognition and worsens over time. Therefore, it is critical to develop a reliable method for early detection of permanent brain atrophy and cognitive impairment. This study used clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer's patients. This audio transcript data were taken from DementiaBank, which is the largest public dataset of AD transcripts. This 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 was compared using two feature sets: the first set consisted of the initial feature set and the second set contained a hybrid feature set that was extracted using the suggested HSI-LFS method. 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 outperformed state-of-the-art models, achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.