An AI-based disease detection and prevention scheme for COVID-19

Sudeep Tanwar; Aparna Kumari; Darshan Vekaria; Neeraj Kumar; Ravi Sharma

doi:10.1016/j.compeleceng.2022.108352

An AI-based disease detection and prevention scheme for COVID-19

Comput Electr Eng. 2022 Oct:103:108352. doi: 10.1016/j.compeleceng.2022.108352. Epub 2022 Sep 2.

Authors

Sudeep Tanwar¹, Aparna Kumari¹, Darshan Vekaria¹, Neeraj Kumar^{2

3}, Ravi Sharma⁴

Affiliations

¹ Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India.
² Thapar Institute of Engineering and Technology, (Deemed to be University), Patiala, Punjab, India.
³ Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
⁴ Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun, India.

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.

Keywords: AI; ARIMA; COVID-19; Disease prediction; Disease prevention; Healthcare 4.0; LSTM.