Excogitation of Stacked Strategy for Coronary Artery Disease Diagnosis

Coronary Artery Disease (CAD) is a dangerous deadly customary disease that prevents the follow of blood to the heart and influenced by several variable risk factors. Recently, machine learning (ML) algorithms assist clinicians for appropriate prediction of CAD by constructing predictive models that may reduce mortality. In this proposed method, UCI data repository and machine learning classifiers are used for prediction. The data available in the UCI repository is preprocessed and updated after feature extraction and validations. Then the processed data is applied to various machine learning classifiers such as eXtreme Gradinet Boosting, Support Vector machine, KNN classifier and stacking algorithm. The stacking algorithm surpassed the results of individual classifier with classification accuracy of 91.8%.