Article icon
Original research

Prediction of heart disease using forest algorithm over K-nearest neighbors using machine learning with improved accuracy


Aim: To perform Predicting heart disease using the Forest algorithm and comparing its feature extraction precision with the K-nearest neighbors algorithm for working on the precision of the forecast. Materials and Methods: In the proposed work, Predicting heart disease was carried out using machine learning algorithms such as K-nearest neighbors algorithm (n=10) and Forest Algorithm (n=10). Here the pretest power examination was done with gpower 80% and the sample size for the two gatherings was 20. Results: From The implemented experiment, the Forest algorithm accuracy is significantly better and it is 90.0% than the K-nearest neighbors algorithm 83.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p<0.05) by performing Independent samples T-tests. Conclusion: The Forest algorithm got better Accuracy and classification of digits better than K-nearest neighbors algorithm for Predicting heart disease.


K N S Shanmukha Raj, K Thinakaran. Prediction of Heart Disease using Forest Algorithm over K-nearest neighbors using Machine Learning with Improved Accuracy. Cardiometry; Issue 25; December 2022; p.1500-1506; DOI: 10.18137/cardiometry.2022.25.15001506; Available from:


Forest Algorithm,   K-Nearest Neighbors Algorithm,   Predicting Heart Disease,   Machine Learning,   Supervised Classification,   Novel Dimensionality Reduction
Download PDF
Current issue
Cardiometry's library
Founders of Cardiometry
Video about Cardiometry
Our partners