Objective: The main purpose of this research is to develop a novel user-friendly web tool based on machine learning approaches, which applies a variety of techniques to address the class imbalance problem. Material and Methods: Shiny, an open- source R package, was used to develop the proposed web tool. The interactive tool can handle the class imbalance problem for binary classification dataset(s) by implementing sampling-based methods. As a clinical application, the dataset retrospectively obtained from the database of the Cardiovascular Surgery Department of Turgut Özal Medical Center, İnönü University, Malatya, Türkiye was used in this web-based software. To overcome the class imbalance prob- lem, sampling-based methods were implemented on the original dataset. After this process, the classification of hypertension in patients with coronary artery disease was achieved by three clas- sification models. Results: According to the outputs of the devel- oped web application, the best classification performance was obtained by the support vector machines with radial basis func- tion kernel (SVM-RBF) model after applying the density-based synthetic minority over-sampling technique oversampling meth- od. The accuracy, sensitivity, specificity, precision, f-measure, and g-mean metrics of the relevant model were calculated as 0.99, 0.99, 0.99, 0.95, 0.97, and 0.97, respectively. Conclusion: The oversampling methods used in this research indicated a more positive contribution to the classification performance of the models as compared to the undersampling methods. When the undersampling methods were applied, the three classification models did not demonstrate successful classification perfor- mance, whereas the SVM-RBF model outperformed the other two models when the oversampling methods were implemented. The designed interactive web application is freely accessible through http://biostatapps.inonu.edu.tr/twoclsbalancer.