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Improved accuracy for predicting the likelihood of Covid-19 using decision tree over K nearest neighbour.
- Source :
-
AIP Conference Proceedings . 2024, Vol. 2729 Issue 1, p1-12. 12p. - Publication Year :
- 2024
-
Abstract
- Aim: To improve the accuracy for predicting the likelihood of covid-19 using Decision Tree over K Nearest Neighbour. Materials and Methods: Decision Tree and K Nearest Neighbour with sample size (N=1810) is executed with varying training and testing splits for predicting the accuracy for Covid-19 prediction. The performance of the classifiers are evaluated based on their accuracy rate using covid-19 symptom dataset. Results and Discussion: The accuracy of predicting Covid-19 in Novel Decision Tree (99%) and K Nearest Neighbour (95%) is obtained. There was a statistical significance between Decision Tree and K Nearest Neighbour(p=0.000). Conclusion: Prediction of Covid-19 using the Novel Decision Tree(DT) algorithm appears to be significantly better than the K Nearest Neighbour(KNN)with improved accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0094243X
- Volume :
- 2729
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- AIP Conference Proceedings
- Publication Type :
- Conference
- Accession number :
- 175307219
- Full Text :
- https://doi.org/10.1063/5.0188484