An Active Learning Machine Technique Based Prediction Of Cardiovascular Heart Disease From Uci-repository Database

This study examines the use of machine learning (ML) techniques for diagnosing and predicting heart disease by analyzing healthcare data. Eight ML classifiers were utilized to identify crucial features that enhance the accuracy of heart disease prediction. Neural network models, such as Naïve Bayes and Radial Basis Functions, were implemented, achieving accuracies of 94.78% and 90.78% respectively in heart disease prediction. Learning Vector Quantization exhibited the highest accuracy rate of 98.7%. The key contributions encompass early intervention, personalized medicine, technological advancements, the impact on public health, and ongoing research, all of which collectively work toward reducing the burden of CHD on both individual patients and society as a whole.