The heart is an essential organ in the human body. On the off chance that this organ gets influenced, at that point, it equally influences the other fundamental pieces of the body. Heart diseases are the front runner in terms of death worldwide, making the need for an effective prediction system a source of high demand in treating affected patients. This study aims to analyze prediction systems, thereby designing an automated medical diagnosis system that takes advantage of the collected database. For this study, ensemble classifiers were implemented for classification of data of a medical database with discretization used during the preprocessing phase. The data employed in this research was obtained from the University of California (UCI) machine learning repository. The dataset utilized was the Statlog heart disease. Performance measures, such as accuracy, sensitivity, and specificity, were used to evaluate the proposed methods’ performance. The proposed method achieved an accuracy of 87.04%. Based on the results obtained, we observed that it is amongst one of the best in comparison with other studies reported on the UCI website.