Implementation of Efficient Machine Learning Techniques for Prediction of Cardiac Disease using SMOTE

Abstract

The leading cause of mortality in the world for the past few decades has been cardiovascular disease (CVD) or cardiac arrest, which is caused by conditions that affect the Heart. With Machine Learning (ML), predictive models are refined and adapted to test health industries in a more efficient way. ML algorithms will helpful to make an early prediction for those who may need to make a change to their lifestyle and receive appropriate medical treatment. Our research presents supervised ML classifiers capable of detecting significant features using machine learning techniques to predict Heart disease. An over sampling method is presented here for learning from unbalanced datasets. Several classification techniques are employed with different features in our prediction model. Performance metrics are analyzed to determine their effectiveness when applied to the UCI heart dataset using ML techniques by SMOTE oversampling; A maximum of 96.6 percentage of accuracy, 90 percentage of sensitivity, and 100 percentage of specificity and precision were achieved by the Random Forest method.