An AI-based disease detection and prevention scheme for COVID-19.
Comput Electr Eng
; 103: 108352, 2022 Oct.
Article
in English
| MEDLINE | ID: covidwho-2007627
ABSTRACT
The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
Comput Electr Eng
Year:
2022
Document Type:
Article
Affiliation country:
J.compeleceng.2022.108352
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