Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, USA.
Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh.
Comput Biol Med. 2022 Jul;146:105657. doi: 10.1016/j.compbiomed.2022.105657. Epub 2022 May 22.
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.
阿尔茨海默病（AD）是全球痴呆症的主要病因，其发病率不断增加，可能超过诊断和管理能力。当 AD 患者因 COVID-19 而死亡时，情况会变得更糟。由于临床特征和患者病情的差异，AD 患者从 COVID-19 中康复的情况因有无 AD 而有很大差异。因此，这种情况刺激了一系列不平衡的数据。类不平衡中包含的不同特征为开发分类框架带来了实质性的问题。本研究提出了一种框架来处理类不平衡，并为混合模型选择最合适和最具代表性的数据集。在这个框架下，利用各种最先进的重采样技术来平衡数据集，并最终选择了三个数据集。我们使用长短期记忆（LSTM）和多层感知机（MLP）算法开发了一种新的混合深度学习模型 AD-CovNet，用于区分 COVID-19 和 AD-COVID-19 患者死亡预测的三个独特数据集。该模型在数据集 I 中实现了 97%的准确率、97%的精度、97%的召回率和 97%的 F1 得分；在数据集 II 中实现了 97%的准确率、97%的精度、96%的召回率和 96%的 F1 得分；在数据集 III 中实现了 86%的准确率、88%的精度、88%的召回率和 86%的 F1 得分。此外，还利用 AdaBoost、XGBoost 和随机森林模型来评估与 AD-COVID-19 患者相关的风险因素，其结果优于诊断性能。模型预测的风险因素具有显著的临床重要性和相关性。此外，还通过统计显著性检验评估了混合模型的性能，并与以前发表的工作进行了比较。总体而言，大型数据集的独特性、深度学习架构的有效性、以及混合模型的准确性和性能展示了首个可以做出更好预测并有助于临床决策的综合工作。
