Deep ensemble learning with transformer models for enhanced Alzheimer’s disease detection

• Published in: Scientific reports Vol. 15; no. 1; pp. 24720 - 19
• Main Authors: Latif, Shiza, Islam, Naeem Ul, Uddin, Zaki, Cheema, Khalid Mehmood, Ahmed, Syed Sohail, Khan, Muhammad Farhan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 692/699; Alzheimer Disease - diagnosis; Alzheimer's disease; Artificial intelligence; Biomarkers; Datasets; Deep Learning; Disease detection; Ensemble Learning; Genetic screening; Humanities and Social Sciences; Humans; multidisciplinary; Natural language processing; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-08362-y

The progression of Alzheimer’s disease is relentless, leading to a worsening of mental faculties over time. Currently, there is no remedy for this illness. Accurate detection and prompt intervention are pivotal in mitigating the progression of the disease. Recently, researchers have been developing new methods for detecting Alzheimer’s at earlier stages, including genetic testing, blood tests for biomarkers, and cognitive assessments. Cognitive assessments involve a series of tests to measure memory, language, attention, and other brain functions. For disease detection, optimal performance necessitates enhanced accuracy and efficient computational capabilities. Our proposition involves the data augmentation of textual data; after this, we deploy our proposed BERT-based deep learning model to make use of its advanced capabilities for improved feature extraction and text comprehension. Our proposed model is a two-branch network. The first branch is based on the BERT encoder, which is used to encode the text data into a fixed-length vector; the BERT output is fed into the convolution layer, followed by the LSTM layer, and finally, the fully connected layer to predict whether a patient has AD or not. The second branch is based on the recurrent convolutional neural network. The recurrent convolutional neural network also takes text data as input and generates the final classification output. Finally, these branches are fused using the ensemble learning approach to give a more robust and accurate output. The proposed model is trained on a corpus of clinical notes from patients with AD and healthy control subjects. Evaluated on the Cookie Theft subset of the DementiaBank Pitt Corpus, our ensemble achieves 94.98% accuracy, 0.9523 F1-score, and 0.93 AUC. The results show that the proposed model outperforms state-of-the-art models for the early diagnosis of AD from text.