Abstract
COVID-19 has severely affected the world health and economic sector. It has also affected the psychological behavior of people of every ages. That’s why this study has been conducted, “Detecting Fear of COVID-19”. A wide variety of data from different ages including student, jobholder, doctor, businessmen, unemployed person, and others is collected for conducting this study. We have collected 553 instances to complete this analysis. By using this data, we have constructed a detection system which help us to detect the fear of COVID-19. We have constructed a machine learning classifier by using ten machine learning algorithms and their features technique. Finally, two machine learning algorithms have been used to identity the fear of human's mind during Covid-19 outbreaks. One is LogitBoost and the other one is Random Forest algorithm. With the assistance of tenfold-cross validation, we have measured the validity of data which is collected by us, whereas performance matrix has helped us to report the evaluation of data. This evaluation report has shown us the accuracy and effectiveness of constructing a model to detect the fear of COVID-19. We have gotten the final result, that is, 70.34% by using LogitBoost algorithm. Our main goal is to identify the fear of COVID-19, as many people were afraid of this virus.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
G. Mertens, L. Gerritsen, S. Duijndam, E. Salemink, I. Engelhard, Fear of the coronavirus (COVID-19): Predictors in an online study conducted in March 2020. J. Anxiety Disord. 74, 102258 (2020)
S. Ren, R. Gao, Y. Chen, Fear can be more harmful than the severe acute respiratory syndrome coronavirus 2 in controlling the corona virus disease 2019 epidemic. World J. Clin. Cases 8(4), 652–657 (2020). Available: https://doi.org/10.12998/wjcc.v8.i4.652
K. Goyal, P. Chauhan, K. Chhikara, P. Gupta, M. Singh, Fear of COVID 2019: first suicidal case in India ! Asian J. Psych. 49, 101989 (2020)
R.B. Duffey, E. Zio, Analysing recovery from pandemics by learning theory: the case of CoVid-19. medRxiv, (2020)
X. Ouyang, J. Huo, L. Xia, F. Shan, J. Liu, Z. Mo, F. Shi, Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Transactions on Medical Imaging (2020)
C. Raju, E. Philipsy, S. Chacko, L.P. Suresh, S.D. Rajan, (March), A survey on predicting heart disease using data mining techniques, in Proceedings of the 2018 Conference on Emerging Devices and Smart Systems (ICEDS) (2018)
S.R. Sonu, V. Prakash, R. Ranjan, K. Saritha, (August), Prediction of Parkinson's disease using data mining, in Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 1082–1085). IEEE.S) (pp. 253–255). IEEE (2017)
, M.Z. Tay, C.M. Poh, L. Rénia, P.A. MacAry, L.F. Ng, The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol. 112 (2020)
Intel. 2020. Logitboost Classifier. [online] Available at: https://software.intel.com/content/www/us/en/develop/documentation/daal-programming-guide/top/algorithms/training-and-prediction/classification/boosting/logitboost-classifier.html [Accessed 17 October 2020]
(1, 2, 3) Jerome Friedman, Trevor Hastie, Robert Tibshirani, Additive logistic regression: a statistical view of boosting. Annals Stat. 28(2), 337–374. JSTOR. Project Euclid (2000)
Medium. 2020. Understanding Random Forest. [online] Available at: https://towardsdatascience.com/understanding-random-forest-58381e0602d2 [Accessed 17 October 2020].
D.L. Dawson, N. Golijani-Moghaddam, COVID-19: psychological flexibility, coping, mental health, and wellbeing in the UK during the pandemic. J. Contextual Behav. Sci. 17, 126–134 (2020)
J.M. Groarke, E. Berry, L. Graham-Wisener, P.E. McKenna-Plumley, E. McGlinchey, C. Armour, Loneliness in the UK during the COVID-19 pandemic: cross-sectional results from the COVID-19 Psychological Wellbeing Study. PloS One, 15(9), e0239698 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Roy, D., Roy, T.J., Mahmud, I., Alvi, N. (2022). An Efficient Approach to Predict Fear of Human’s Mind During COVID-19 Outbreaks Utilizing Data Mining Technique. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1388. Springer, Singapore. https://doi.org/10.1007/978-981-16-2597-8_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-2597-8_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2596-1
Online ISBN: 978-981-16-2597-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)