  Heart disease is the major cause of non-communicable and silent death
worldwide. Heart diseases or cardiovascular diseases are classified into four
types: coronary heart disease, heart failure, congenital heart disease, and
cardiomyopathy. It is vital to diagnose heart disease early and accurately in
order to avoid further injury and save patients' lives. As a result, we need a
system that can predict cardiovascular disease before it becomes a critical
situation. Machine learning has piqued the interest of researchers in the field
of medical sciences. For heart disease prediction, researchers implement a
variety of machine learning methods and approaches. In this work, to the best
of our knowledge, we have used the dataset from IEEE Data Port which is one of
the online available largest datasets for cardiovascular diseases individuals.
The dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland
& Statlog datasets with important features such as Maximum Heart Rate Achieved,
Serum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess
the efficacy and strength of the developed model, several performance measures
are used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and
accuracy. In this study, we have proposed a framework with a stacked ensemble
classifier using several machine learning algorithms including ExtraTrees
Classifier, Random Forest, XGBoost, and so on. Our proposed framework attained
an accuracy of 92.34% which is higher than the existing literature.
