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

Shamima Akter; Depro Das; Rakib Ul Haque; Mahafujul Islam Quadery Tonmoy; Md Rakibul Hasan; Samira Mahjabeen; Manik Ahmed

doi:10.1016/j.compbiomed.2022.105657

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

Comput Biol Med. 2022 Jul:146:105657. doi: 10.1016/j.compbiomed.2022.105657. Epub 2022 May 22.

Authors

Shamima Akter¹, Depro Das², Rakib Ul Haque³, Mahafujul Islam Quadery Tonmoy⁴, Md Rakibul Hasan⁵, Samira Mahjabeen⁵, Manik Ahmed⁶

Affiliations

¹ Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, USA. Electronic address: sakter5@gmu.edu.
² Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh.
³ School of Computer Science & Technology, University of Chinese Academy of Sciences, Beijing, 100049, China.
⁴ Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh.
⁵ Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh.
⁶ Department of Civil Engineering, Virginia Tech, USA.

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.

Keywords: AD-CovNet; Alzheimer's disease; COVID-19; Deep learning; Fatality; LSTM; MLP; Mortality; Risk factors.

MeSH terms

Alzheimer Disease*
COVID-19*
Deep Learning*
Humans
Neural Networks, Computer
Risk Factors