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. 2022 May:100:107971.
doi: 10.1016/j.compeleceng.2022.107971. Epub 2022 Apr 6.

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

Roa'a Mohammedqasem  1 , Hayder Mohammedqasim  1 , Oguz Ata  1
Affiliations

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network

Roa'a Mohammedqasem et al. Comput Electr Eng. 2022 May.

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.

Keywords: ANN, Artificial Neural Network; AUC, Area Under Curve; CNN, Convolutional Neural Network; COVID-19; COVID-19, Coronavirus disease; DL, Deep learning; Imbalanced Dataset; Internet of Things; IoT, Internet of Things; ML, Machine learning; RFE, Recursive Feature Elimination; RNN, Recurrent Neural Network; Recursive feature elimination; SMOTE, Synthetic Minority Oversampling Technique; Synthetic minority oversampling technique.

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Conflict of interest statement

The authors whose names are listed immediately below certify that they have no Affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, Employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or Materials discussed in this manuscript.

Figures

Fig. 1
Fig. 1
Machine learning flowchart for COVID-19 models.
Fig. 2:
Fig. 2
AUC graph analysis for all models.
Fig. 3:
Fig. 3
Deep learning loss graphs.
Fig. 4:
Fig. 4
Difference between training and test accuracy.
See this image and copyright information in PMC

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