Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images K Sujatha N.P.G. Bhavani D. Kirubakaran N. Janaki G.Victo Sudha George Su-Qun Cao A. Kalaivani Abstract

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.
05 01 2023 012001 http://dx.doi.org/10.1088/crossmark-policy iopscience.iop.org Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images Journal of Physics: Conference Series paper Published under licence by IOP Publishing Ltd http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining 10.1088/1742-6596/2467/1/012001 https://iopscience.iop.org/article/10.1088/1742-6596/2467/1/012001 https://iopscience.iop.org/article/10.1088/1742-6596/2467/1/012001/pdf https://iopscience.iop.org/article/10.1088/1742-6596/2467/1/012001/pdf https://iopscience.iop.org/article/10.1088/1742-6596/2467/1/012001 https://iopscience.iop.org/article/10.1088/1742-6596/2467/1/012001/pdf Journal of Emergency Medicine Eastin 58 713 2020 10.1016/j.jemermed.2020.04.007 Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China J. Med. Virol. Wang 92 568 2020 10.1002/jmv.25748 Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures J Clin Invest. Chen 130 2620 2020 10.1172/JCI137244 Clinical and immunological features of severe and moderate coronavirus disease 2019 Computer Methods and Programs in Biomedicine Khan 196 2020 10.1016/j.cmpb.2020.105581 Coronet: a deep neural network for detection and diagnosis of COVID - 19 from chest x - ray images J Infect Dis. Heimdal 219 1198 2019 10.1093/infdis/jiy646 Human Coronavirus in Hospitalized Children with Respiratory Tract Infections: A 9-Year Population-Based Study from Norway. Physiological Genomics Alimadadi 52 200 2020 10.1152/physiolgenomics.00029.2020 Artificial Intelligence and Machine Learning to Fight COVID-19 J Infect Dis. Monto 222 9 2020 10.1093/infdis/jiaa161 Coronavirus Occurrence and Transmission Over 8 Years in the HIVE Cohort of Households in Michigan Am J Epidemiol. McIntosh 91 585 1970 10.1093/oxfordjournals.aje.a121171 Seroepidemiologic studies of coronavirus infection in adults and children. J. Infect Dis. Nickbakhsh 222 17 2020 10.1093/infdis/jiaa185 Epidemiology of Seasonal Coronaviruses: Establishing the Context for the Emergence of Coronavirus Disease 2019 Lancet Glob Health Clark 1003 2020 10.1016/S2214-109X(20)30264-3 Centre for the Mathematical Modelling of Infectious Diseases COVID-19 working group. Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study IEEE/ACM Transactions on Computational Biology and Bioinformatics Song 18 2775 2021 10.1109/TCBB.2021.3065361 Deep learning enables accurate diagnosis of novel coronavirus(covid-19) with CT images European Radiology Wang 31 6096 2021 10.1007/s00330-021-07715-1 A deep learning algorithm using CT images to screen for corona virus disease IEEE Transactions on Pattern Analysis and Machine Intelligence Hu 42 2011 2020 10.1109/TPAMI.2019.2913372 Squeeze-and- excitation networks