In recent years, there have been great improvements in data classification processes using machine learning methods. As technological advances increase, the size of data in the internet and other environments also increases rapidly. With these developments, unbalanced and unclassified data has emerged. The problem of imbalance is that one of the two classes has fewer samples than the other. Most of the datasets, especially used in the medical field, have an unbalanced distribution. A dataset with unbalanced distribution negatively affects the performance of classification algorithms. Many studies have been conducted to balance and classify this distribution. These studies are at the data and algorithm level and are undersampling and oversampling processes. In this study, the existing samples belonging to the minority class were resampled synthetically, and the datasets were balanced. For the resampling process, among the samples belonging to the minority class, the closest neighbors were determined for all data points using the Euclidean distance metric. Based on these neighbors, the desired number of new synthetic samples were created between each sample using the Weighted Geometric Mean. As a result of this process, the dataset has been balanced. In addition, Random Undersampling (RUS), Random Oversampling (ROS), and Synthetic Minority Sampling Technique (SMOTE) methods are also used to balance the datasets. The raw and balanced datasets are classified using the Random Forest algorithm, and the results are compared. As a result of the study, an increase is observed in all performance values of the datasets balanced with the new resampling approach. Using the approach proposed in the study, it is shown that the balanced datasets using the new resampling method improve the classification performance compared to the raw dataset and other methods.