Improved accuracy for predicting the likelihood of Covid-19 using decision tree over K nearest neighbour

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.