Heart disease is the leading cause of death around the globe, killing more people than cancer. A study published in the Journal of the American Heart Association shows that gene variants in our genes can increase our risk of heart failure by as much as 40%. To make matters worse, the number of people with advanced heart failure is expected to rise by 30% by the year 2030. Some of the most common heart disease symptoms include chest pain, shortness of breath, and fatigue. It has been observed

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... t machine learning can provide efficient heart disease prediction over a large amount of data thereby, empowering the patients and health care experts with the knowledge to make more accurate decisions at an appropriate time-bound. The central aspect of our proposed research is to build up a hybrid machine learning classifier using a Genetic Algorithm with a Super learner ensemble (GA-SLE) for the detection of heart disease with increased accuracy. In our study, the proposed system is compared with various machine learning algorithms such as Random Forest (RF), Multilayer Perceptron (MLP), K- Nearest Neighbour (KNN), Extra Tree (ET), Extreme Gradient Boosting (XGB), Super Vector Classifier (SVC), Stochastic Gradient Descent(SGD), AdaBoost, Decision Tree(CART), Gradient Boosting(GBM) using 10-Fold cross-validation and hyperparameter tuning. A regressed comparative analysis of all three approaches has been performed with the help of a table and plot. The proposed GA-SLE outperforms all other ML classifiers, obtaining a prediction accuracy of 99.8% with minimum error loss. As per our findings, the current GA-SLE classifier enables the practitioner to detect and diagnose the sickness in the early stage of the disease.