Published April 22, 2022 | Version v1
Journal article Open

A Novel Corona Virus Detection and Validation Measures using Machine Learning Techniques

  • 1. Department of Computer Science & Engineering, Krishna University, Andhra Pradesh, India.

Description

Data mining is a process of extracting unknown or hidden knowledge from the existing data. This is mainly used for predicting the future based on the past data. Classification in data mining is a common technique that classifies data instances into different classes. It allows you to organize data sets of all sorts, including complex and large datasets as well as small and simple one. Decision tree uses the data to generate sequence of if else rules for decision making. In this paper, we are discussing the pandemic covid-19 related dataset of 1,81,884 instances with 9 attributes. The real world covid-19 data is used to build model to extract the important rules about who had a likely chance to get covid-19 positive. This paper includes one of the algorithms of the decision tree known as C4.5.The experimental results provide are good set of rules for corona virus detection.

Files

A Novel corona virus detection -Formatted Paper.pdf

Files (186.3 kB)

Additional details

References

  • Mahmood, A. M., & Kuppa, M. R. (2010, December). Early detection of clinical parameters in heart disease by improved decision tree algorithm. In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems (pp. 24-29). IEEE.
  • Mahmood, A. M., & Kuppa, M. R. (2012). A novel pruning approach using expert knowledge for dataspecific pruning. Engineering with Computers, 28(1), 21-30.
  • Mahmood, A. M., & Kuppa, M. R. (2012). A novel pruning approach using expert knowledge for dataspecific pruning. Engineering with Computers, 28(1), 21-30.
  • Mahmood, A. M., & Kuppa, M. R. (2010, December). Early detection of clinical parameters in heart disease by improved decision tree algorithm. In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems (pp. 24-29). IEEE.
  • Mahmood, A. M., Rao, K. M., & Reddi, K. K. (2010). A novel algorithm for scaling up the accuracy of decision trees. International Journal on Computer Science and Engineering, 2(2), 126-131.
  • Mahmood, A. M., Kuppa, M. R., & Reddi, K. K. (2010). A New decision Tree Induction Using Composite Splitting Criterion. Journal of Applied Computer Science & Mathematics, (9).
  • Reddi, K. K., Mahmood, A. M., & Rao, K. M. (2010). Generating optimized decision tree based on discrete wavelet transform. (IJEST) International Journal of Engineering Science and Technology, 2(3), 157- 164.
  • Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). COVID-19 Prediction applying supervised machine learning algorithms with comparative analysis using WEKA. Algorithms, 14(7), 201.
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.
  • Pulgar-Sánchez, M., Chamorro, K., Fors, M., Mora, F. X., Ramírez, H., Fernandez-Moreira, E., & Ballaz, S. J. (2021). Biomarkers of severe COVID- 19 pneumonia on admission using data-mining powered by common laboratory blood testsdatasets. Computers in biology and medicine, 136, 104738.