Prediction of Heart Disease using Decision Tree in Comparison with KNN to Improve Accuracy

Abstract

Aim: The aim of this survey is to use the irregular forest method to forecast cardiac disease and to achieve better forecasting accuracy by implementing machine learning algorithms and comparing their performance to KNN. In this research, two groups—Decision Tree and K-Nearest Neighbor—are examined. The strategies have been put into practice and tested using a dataset with 1700 records. N=20 iterations on each method are run as part of the programming experiment to identify different grades of model performance with a repeated measures power of 80%. Following testing, the decision tree method and the k-nearest neighbor approach both achieve mean effectiveness of 86.7 percent and 82.5 percent, respectively, for the disease prediction. By using independent samples t-tests, it can be shown that the accuracy of the two algorithms differs statistically significantly (p 0.05). The results of the comparison demonstrate that the Random Forest algorithm performs noticeably better than KNN.