Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network. (May 2022)

• Record Type: Journal Article
• Authors: Mohammedqasem, Roa'a; Mohammedqasim, Hayder; Ata, Oguz
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: COVID-19 -- Synthetic minority oversampling technique -- Recursive feature elimination -- Imbalanced Dataset -- Internet of Things; DL Deep learning -- ML Machine learning -- ANN Artificial Neural Network -- CNN Convolutional Neural Network -- RNN Recurrent Neural Network -- IoT Internet of Things -- AUC Area Under Curve -- SMOTE Synthetic Minority Oversampling Technique -- RFE Recursive Feature Elimination -- COVID-19 Coronavirus disease; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107971
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.