Heart diseases are the leading cause of death worldwide. Nowadays, heart diseases are killing more people than ever before. Thus, the authors of this study designed this project to analyze data on heart diseases prediction. The project uses raw data in the form of a.csv file as data set. The authors collected the used dataset from the cardiology department of the Graben University Clinic (Butembo/DR Congo) that included 389 records and 25 variables including age, employment, pulse rate, blood pressure and clinical symptoms. The aim was to compare Machine Learning (ML) ensemble methods such as Boosting type (AdaBoosting, GradientBoosting and XGBoosting) with single ML models (KNN, Stochastic gradient descent (SGD), Decision Tree) to see which of the models predict better heart diseases in unstable and insecure areas. Thus, the results showed that the XGBoost model performed better with accuracy, precision and recall of 85% respectively. In this research the authors concluded that Boosting as ensemble method classifies accurately heart diseases data in an insecure area such as Butembo, in the province of North-Kivu, DR Congo.