AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19. (July 2022)

• Record Type: Journal Article
• Authors: Akter, Shamima; Das, Depro; Haque, Rakib Ul; Quadery Tonmoy, Mahafujul Islam; Hasan, Md Rakibul; Mahjabeen, Samira; Ahmed, Manik
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: COVID-19 -- AD-CovNet -- Alzheimer's disease -- Deep learning -- Mortality -- Fatality -- MLP -- LSTM -- Risk factors; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105657
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Alzheimer's disease (AD) is the leading cause of dementia globally, with a growing morbidity burden that may exceed diagnosis and management capabilities. The situation worsens when AD patient fatalities are exposed to COVID-19. Because of differences in clinical features and patient condition, a patient's recovery from COVID-19 with or without AD varies greatly. Thus, this situation stimulates a spectrum of imbalanced data. The inclusion of different features in the class imbalance offers substantial problems for developing of a classification framework. This study proposes a framework to handle class imbalance and select the most suitable and representative datasets for the hybrid model. Under this framework, various state-of-the-art resample techniques were utilized to balance the datasets, and three sets of data were finally selected. We developed a novel hybrid deep learning model AD-CovNet using Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP) algorithms that delineate three unique datasets of COVID-19 and AD-COVID-19 patient fatality predictions. This proposed model achieved 97% accuracy, 97% precision, 97% recall, and 97% F1-score for Dataset I; 97% accuracy, 97% precision, 96% recall, and 96% F1-score for Dataset II; and 86% accuracy, 88% precision, 88% recall, and 86% F1-score for Dataset III. In addition, AdaBoost, XGBoost, and Random Forest models were utilized to evaluate the risk factors associated with AD-COVID-19 patients, and the outcome outperformed diagnostic performance. The risk factors predicted by the models showed significant clinical importance and relevance to mortality. Furthermore, the proposed hybrid model's performance was evaluated using a statistical significance test and compared to previously published works. Overall, the uniqueness of the large dataset, the effectiveness of the deep learning architecture, and the accuracy and performance of the hybrid model showcase the first cohesive work that can formulate better predictions and help in clinical decision-making. Graphical abstract: Exploratory analysis of COVID-19 patient with Alzheimer's disease using AD-CovNet. Image 1 Highlights: A novel hybrid deep learning (DL) framework for predicting COVID fatality in Alzheimer's patients. State-of-the-art resample techniques to balance the dataset, and statistical evaluations are performed. Live dataset and 38 features are used, and hyperparameter tune-up is utilized to enhance performance. Risk factors are identified, and clinical significance is discussed. The proposed AD-CovNet model shows superior performance.