Heart disease prediction using ensemble methods of machine learning.

Heart diseases are very dangerous for humans, as these are very serious and can even lead to the death of the patient. In today's world, Machine Learning has a vital role in bioscience as well. This paper presents a comparative analysis to detect cardiovascular disease using average Ensemble Learning, Random Forest and extreme Gradient Boosting Algorithms. The Normalization method is used to preprocess the used dataset and then all the techniques mentioned above are used to train the model. By using all three techniques/ methods accuracy, recall, precision, ROC, and F-measure, are evaluated. Boosting ensemble learning method performed best among all with an exactness/accuracy score of 91.2%. [ABSTRACT FROM AUTHOR]