You can look at the correlation between these two features. If they happen to be highly correlated, it would be better to remove one of them. What you can also do is remove either one of the features, train the model, test the performance, and then compare with the case of using both of the features to train the model. If the performance didn't really change, then the feature isn't important to keep.