Cardiovascular disease is one of the most dangerous diseases that lead to death. It results from the lack of early detection of heart patients. Many researchers analyzed the risk factors of cardiovascular disease and proposed machine learning models for the early detection of heart patients. However, these models suffer from the high dimensionality of data and need to be improved to obtain highly accurate results. In this paper, a practical proposal is presented that can predict whether a patient has cardiovascular disease or not. The proposal was tested using five different standard data sets from the UCI repository. Our proposal consists of two main processes: the first is the data preprocessing process, and the second is the prediction process. In data preprocessing, the data is prepared for the prediction process, and three different feature selection methods (e.g., PCA) are applied to select the most relevant features from the data. In the prediction process, fourteen different prediction techniques (for example, Random Forest (RF) and Support Vector Classifier (SVC)) were applied to over-employed datasets. The techniques used were evaluated using four evaluation metrics: accuracy, precision, recall, and F1-score. The experimental results show that the LASSO method as a feature selection method with RF as a prediction technique produced the best accuracy (100%). Accuracy (99.57%) was obtained for Decision Tree (DT), Gradient Boosting (GB), AdaBoost (AB), Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM). The accuracy of SVC, Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Classifier Bagging Method (SVCBM) was very similar to each other (98.73%).