Abstract
Coronaviruses are a large family of viruses that can cause a human being to become critically sick. In different forms, COVID-19 affects various people. Since COVID-19 has begun its rampant expansion, isolating COVID-19 infected individuals is the best way to deal with it. This can be accomplished by monitoring individuals by running recurring COVID tests. The use of computed tomography (CT-scan) has demonstrated good results in evaluating patients with possible COVID-19 infection. Patients with COVID will heal with the help of antibiotic therapy from vitamin C supplements. Patients with these symptoms need a faster response using non-clinical methods such as machine learning and deep neural networks in order to manage and address additional COVID-19 spreads worldwide. Here, in this paper we are diagnosis the covid-19 patients with CT-scan images by applying XGBoost classifier. Developed a web application which basically accepts a patient CT-scan to classify COVID positive or negative. After that, the negative class patients with symptoms are suggested with a danger rate with the help of age groups, health-related issues, and the area he/she belongs to. Three machine learning algorithms, the decision tree, random forest, and k-nearest neighbor algorithms, were used for this. The results of the current investigation showed that the model built with the decision tree data mining algorithm is more fruitful in foreseeing the risk rate of 93.31 percent overall accuracy of infected patients.
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References
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system, KDD ’16, August 13–17, 2016, San Francisco, CA, USA
Gorbalenya, A.E., Baker, S.C., Baric, R.S., de Groot, R.J., Drosten, C., Gulyaeva, A.A., et al.: Severe acute respiratory syndrome-related coronavirus: the species and its viruses—a statement of the coronavirus study group (2020)
Corman, V.M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D.K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M.L., Mulders, D.G., D.G.J.C., Mulders, B.L. Haagmans, B. van der Veer, S. van den Brink, Wijsman, L., Goderski, G., Romette, J.-L., Ellis, J., Zambon, M., Peiris, M., Goossens, H., Reusken, C., Koopmans, M.P.G., Drosten, C.: Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro. J. Infect. Dis. Surveill. Epidemiol. Prev. Control 25(3) (2020)
Eisenhofer, R., Weyrich, L.S.: Assessing alignment-based taxonomic classification of ancient microbial DNA. PeerJ 7 (2019)
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N., et al.: Chest CT findings in coronavirus disease-19 (covid19): Relationship to duration of infection. Radiology 0, 200463 (2020)
He, X., Yang, X., Zhang, S., Zhao, J., Zhang, Y., Xing, E., et al.: Sample-efficient deep learning for covid-19 diagnosis based on CT scans (2020)
Ozkaya, U., Ozturk, S., Barstugan, M.: Coronavirus (covid-19) classification using deep features fusion and ranking technique (2020)
Randhawa, G.S., Soltysiak, M.P.M., El Roz, H., de Souza, C.P.E., Hill, K.A., Kari, L.: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS ONE 15(4) (2020)
Al-Rousan, N., Al-Najjar, H.: Now casting and forecasting the spreading of novel coronavirus 2019-nCoV and its association with weather variables in 30 Chinese provinces: a case study (2020)
Zhu, X., Zhang, A., Xu, S., Jia, P., Tan, X., Tian, J., et al.: Spatially explicit modeling of 2019-nCoV epidemic trend based on mobile phone data in Mainland China. medRxiv (2020)
Backer, J.A., Klinkenberg, D., Wallinga, J.: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan China 20–28 January 2020. Eurosurveillance 25(5), 10–15 (2020)
Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P.D., Zhang, H., Ji, W., et al.: Rapid Ai development cycle for the coronavirus (Covid-19) pandemic: initial results for automated detection patient monitoring using deep learning CT image analysis (2020)
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Indira, D.N.V.S.L.S., Abinaya, R. (2022). CoviNet: Role of Convolution Neural Networks (CNN) for an Efficient Diagnosis of COVID-19. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_18
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DOI: https://doi.org/10.1007/978-981-16-6624-7_18
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