International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Classification of COVID-19 Disease: A Machine Learning Perspective

  • Kinza Sardar (Department of Software Engineering, The University of Lahore)
  • 투고 : 2024.03.05
  • 발행 : 2024.03.30
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초록

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

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참고문헌

  1. Who, "Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003." WHO Geneva, Switzerland, 2003.
  2. K. Roosa et al., "Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020," Infect. Dis. Model., vol. 5, pp. 256-263, 2020.
  3. W. Kong and P. P. Agarwal, "Chest imaging appearance of COVID-19 infection," Radiol. Cardiothorac. Imaging, vol. 2, no. 1, p. e200028, 2020.
  4. T. Singhal, "A review of coronavirus disease-2019 (COVID-19)," Indian J. Pediatr., pp. 1-6, 2020.
  5. E. Y. P. Lee, M.-Y. Ng, and P.-L. Khong, "COVID-19 pneumonia: what has CT taught us?," Lancet Infect. Dis., vol. 20, no. 4, pp. 384-385, 2020. https://doi.org/10.1016/S1473-3099(20)30134-1
  6. J. F.-W. Chan et al., "A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster," Lancet, vol. 395, no. 10223, pp. 514-523, 2020. https://doi.org/10.1016/S0140-6736(20)30154-9
  7. O. Faust, Y. Hagiwara, T. J. Hong, O. S. Lih, and U. R. Acharya, "Deep learning for healthcare applications based on physiological signals: A review," Comput. Methods Programs Biomed., vol. 161, pp. 1-13, 2018. https://doi.org/10.1016/j.cmpb.2018.04.005
  8. P. Herron, "Machine learning for medical decision support: evaluating diagnostic performance of machine learning classification algorithms," INLS 110, Data Min., pp. 1-16, 2004.
  9. J. Huang and C. X. Ling, "Using AUC and accuracy in evaluating learning algorithms," IEEE Trans. Knowl. Data Eng., vol. 17, no. 3, pp. 299-310, 2005. https://doi.org/10.1109/TKDE.2005.50
  10. D. Gil, K. Diaz-Chito, C. Sanchez, and A. Hernandez-Sabate, "Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images," arXiv Prepr. arXiv2005.13928, 2020.
  11. F. Shi et al., "Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification," arXiv Prepr. arXiv2003.09860, 2020.
  12. M. Barstugan, U. Ozkaya, and S. Ozturk, "Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods," no. 5, pp. 1-10, 2020, [Online]. Available: http://arxiv.org/abs/2003.09424.
  13. E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, "Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images," arXiv Prepr. arXiv2003.11055, 2020.
  14. J. Zhou, B. Jing, and Z. Wang, "SODA: Detecting Covid19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation," 2020, [Online]. Available: http://arxiv.org/abs/2005.11003.
  15. I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635-640, 2020, doi: 10.1007/s13246-020-00865-4.
  16. I. D. Apostolopoulos, S. I. Aznaouridis, and M. A. Tzani, "Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases," J. Med. Biol. Eng., p. 1, 2020.
  17. T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Comput. Biol. Med., p. 103792, 2020.
  18. F. Shan et al., "Lung infection quantification of covid-19 in ct images with deep learning," arXiv Prepr. arXiv2003.04655, 2020.