The medical industry arguably generates the largest amount of data on a daily basis. Extraction of new and useful information from the bulk of data generated is very tedious. Although, it contributes to the quality of service rendered in the health sector. Data mining techniques are among the major approach that shows promising result when applied in diagnosing patient and prediction of diseases. In this study, AdaBoost and Bagging are used to support classifiers such as Naïve Bayes, Neural Network in prediction of heart disease while Random Forest was applied separately. Comparison of the experiment results focus majorly on the ensemble method used (AdaBoost and Bagging). With respect to this study, Bagging outperforms AdaBoost in term of Accuracy and other parameters such as Kappa Statistics, weighted average of ROC, Precision and MCC. It is therefore recommended as a good supportive technique for weak classifiers. Although, both Bagging and AdaBoost decline in performance when applied on rigorous dataset.