The existence of many features refracts the manual interpretation process of the Cardiotocography (CTG) data. Therefore, feature selection methods are useful to select the relevant features that can reduce the complexity of the interpretation. The reduction of the complexity also speeds up time computation besides improving the accuracy of the classification and prediction results. This study proposes a statistical approach by using the feature selection method based on F-Score. The method aims to tackle the imbalanced data with multi-class output. In this method, the features will be assessed individually and rated based on their F-score. The features with an F-score value above the average will be chosen as the relevant features. We use Support Vector Machine (SVM) as a classifier to implement the F-score method. The experiment also employed other datasets to test the compatibility of the F-score method. The scalability and stability testing conducted to evaluate the performance of the F-score method. The experiment result shows that the F-score method can be implemented successfully. In the case of CTG dataset, the accuracy of the classifier improves from 94.35% by using 21 features to 99.91% by using eight relevant features. This improvement also can be found in all of the dataset experiment results.