def METHOD_NAME(annotation_file, target_annotation_folder, segmented_image_folder, target_image_folder, split_ratio):
    """
    Performs data preparation for breast cancer prediction, including creating training and testing data.
    Args:
        annotation_file (str): Location of the annotation file.
        target_annotation_folder (str): Location to save the processed annotation files.
        segmented_image_folder (str): Location of the folder containing segmented images.
        target_image_folder (str): Location to store the train and test images.
        split_ratio (float): Split ratio of the test data.
    Returns:
        None
    """
    input_data = read_csv(annotation_file)
    # create training and testing data
    training_data, testing_data = create_train_and_test_nlp(input_data, split_ratio)
   # get the path to training and testing data
    training_data_path = path.join(target_annotation_folder, "training_data.csv")
    testing_data_path = path.join(target_annotation_folder, "testing_data.csv")
    # save training and testing data
    training_data.to_csv(training_data_path, index=False)
    testing_data.to_csv(testing_data_path, index=False)
    # Get the list of class labels and the column containing the class label
    label_column = 'label'
    label_list = np.sort(input_data[label_column].unique()).tolist()
    # create vision data    
    create_train_and_test_vision_data(label_column, label_list, training_data, testing_data, segmented_image_folder, target_image_folder)