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

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,...

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Veröffentlicht in:	Computers in biology and medicine 2022-07, Vol.146, p.105657-105657, Article 105657
Hauptverfasser:	Akter, Shamima, Das, Depro, Haque, Rakib Ul, Quadery Tonmoy, Mahafujul Islam, Hasan, Md Rakibul, Mahjabeen, Samira, Ahmed, Manik
Format:	Artikel
Sprache:	eng
Schlagworte:	AD-CovNet Algorithms Alzheimer Disease Alzheimer's disease Coronaviruses COVID-19 Datasets Decision making Decision trees Deep Learning Dementia disorders Fatalities Fatality Humans Long short-term memory LSTM Machine learning MLP Model accuracy Morbidity Mortality Multilayer perceptrons Multilayers Neural Networks, Computer Neurodegenerative diseases Patients Performance evaluation Recall Risk analysis Risk Factors
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