Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques

Expert Systems - Tập 39 Số 3 - 2022
Dimple Tiwari1, Bhoopesh Singh Bhati1, Fadi Al‐Turjman2, Bharti Nagpal1
1Ambedkar Institute of Advanced Communication Technologies and Research, Govt. of NCT of Delhi, Delhi, India
2Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Turkey

Tóm tắt

AbstractPandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

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Tài liệu tham khảo

10.1016/j.egypro.2017.07.482

10.1620/tjem.210.285

10.1152/physiolgenomics.00029.2020

10.1109/IOTM.0001.2000123

10.1016/j.procs.2016.04.224

10.1001/jama.2020.2565

Bhatla N., 2012, An analysis of heart disease prediction using different data mining techniques, International Journal of Engineering, 1, 1

10.1016/j.scitotenv.2020.138817

10.1109/ACCESS.2017.2694446

10.1016/j.chaos.2020.109864

Coronavirus Outbreak. (2020). Retrieved fromhttps://www.worldometers.info/coronavirus/. Accessed 25‐05‐2020.

Coronavirus Symptoms Information. (2020). Retrieved fromhttps://en.wikipedia.org/wiki/Coronavirus_disease_2019

10.1016/j.cmpb.2016.10.011

10.4066/biomedicalresearch.29-18-620

10.1016/S2213-2600(20)30116-8

10.1109/ICoAC.2013.6921958

10.3390/math8081268

Hu Z. Ge Q. Jin L. &Xiong M.(2020).Artificial intelligence forecasting of covid‐19 in china.arXiv preprint arXiv:2002.07112.

Huang M. W., 2017, SVM and SVM ensembles in breast cancer prediction, PLoS One, 12, 10.1371/journal.pone.0161501

10.1016/j.ijmedinf.2020.104141

10.1109/JSEN.2020.3030905

10.4103/ijccm.IJCCM_279_17

10.1016/j.csbj.2014.11.005

10.1016/j.cogsys.2018.12.001

10.1016/S1473-3099(20)30144-4

10.1016/j.chaos.2020.110059

10.1016/j.cca.2020.03.022

10.1016/j.eswa.2015.06.024

10.1016/j.tmaid.2020.101742

10.1016/j.asoc.2020.106613

Medhekar D. S., 2013, Heart disease prediction system using naive Bayes, International Journal of Enhanced Research In Science Technology & Engineering, 2, 1

10.3390/healthcare8020181

10.1016/j.jclepro.2020.122807

10.1109/IACC48062.2019.8971567

Naïve Bayes information. (2020). Retrieved fromhttps://visualstudiomagazine.com/articles/2019/05/01/naive-bayes-classification.aspx

10.1016/j.compchemeng.2017.06.011

10.1016/j.bbe.2017.09.002

Patel J., 2015, Heart disease prediction using machine learning and data mining technique, Heart Disease, 7, 129

Pattekari S. A., 2012, Prediction system for heart disease using Naïve Bayes, International Journal of Advanced Computer and Mathematical Sciences, 3, 290

Peng L. Yang W. Zhang D. Zhuge C. &Hong L.(2020).Epidemic analysis of COVID‐19 in China by dynamical modeling.arXiv preprint arXiv:2002.06563.

10.1371/journal.pone.0231236

10.1016/j.scs.2020.102372

10.1016/j.asoc.2020.106441

10.1016/j.tmaid.2020.101623

10.1016/j.jaut.2020.102433

10.1175/JCLI-D-16-0012.1

10.1007/s00134-020-05991-x

10.1016/j.chaos.2020.109945

10.1007/s12098-020-03263-6

Sonavane S., 2013, Novel approach for localization of indian car number plate recognition system using support vector machine, International Journal of Advanced Research in Computer Science and Software Engineering, 3, 179

Srivastava V., 2020, A systematic approach for COVID‐19 predictions and parameter estimation, Personal and Ubiquitous Computing, 25, 1

Tiwari D., 2020, Artificial intelligence and machine learning for COVID‐19, 97

10.1016/j.iot.2020.100222

10.1016/j.dsx.2020.04.012

10.1097/00003246-200006000-00031

Vijayarani S., 2015, Liver disease prediction using SVM and Naïve Bayes algorithms, International Journal of Science, Engineering and Technology Research (IJSETR), 4, 816

Vijayarani S., 2015, Kidney disease prediction using SVM and ANN algorithms, International Journal of Computing and Business Research (IJCBR), 6, 1

10.1016/S0140-6736(20)30185-9

10.1016/j.chaos.2020.110058

10.1002/jmv.25689

10.3354/cr030079

World Health Organization Situation reports. (2020). Retrieved fromhttps://www.who.int/emergencies/diseases/novel-Coronavirus-2019/situation-reports/

Wynants L., 2020, Prediction models for diagnosis and prognosis of covid‐19: Systematic review and critical appraisal, BMJ, 369, 1

10.1016/j.ijid.2020.03.017

10.21037/jtd.2020.02.64

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Expert Systems

Tập 39 Số 3

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