Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data

Abstract

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.

A Novel Dual Feature Extraction using Fine-Tuned ResNet with GWO and Deep Dense Neural Network for Multiple Lung Disease Classification

“…CAD for chest X-rays has the potential to develop into a useful aid for radiologists that is also affordable [11]. Deep learning technologies are superior in addressing a variety of X-ray analysis problems, including image classification [12,13], NLP-based analysis [14], and localization [15], according to recent developments in artificial intelligence and machine learning. The growing amount of publicly available medical imaging datasets, including CT, MRI, and X-ray images, benefits deep learning's data-driven approach [16].…”

Adversarial Network-Based Classification for Alzheimer’s Disease Using Multimodal Brain Images: A Critical Analysis

“…Meanwhile, ADNI [1], [45], [47], and the Open Access Series of Imaging Studies (OASIS) [36], [37] stand out as significant contributors to advancing research in this domain. Structural MRI [2], [18], [27], [32], [33], [36], [39] OASIS MRI MRI images with a focus on brain tumor detection Structural MRI [5], [8], [14], [15], [17], [19], [21], [22], [24], [25], [36], [37], [38], [42], [43] MRI ADNI Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset Functional and Structural MRI, PET [1], [4], [4], [7], [16], [23], [28], [29], [30], [40], [41], [44], [45] ADNI GARD Gwangju Alzheimer's and Related Dementia (GARD) dataset…”

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

“…In short, in the application scenario of nursing homes, the existing research cannot meet the needs of using audio for disease risk prediction, and the Risevi model we propose meets the current application needs of nursing homes. [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”

“…Disease Basic Algorithm Accuracy M. V. A. Rao [10] Asthma SVR 77.77% Y. You [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”