Which feature to use in feature selection?
My guess would be that the features are highly correlated. Check this. Regarding feature selection, I suggest starting with Logit and Lasso (L1 regulation). This method (Lasso, l1) can „shrink“ features to zero (so kick them out basically). This happens automatically based on feature importance.

Many methods (e.g. Boosting, Neural Nets) allow you to use L1 regularization. So there may be no need to „manually“ select features.

If you want to do that manually, you may use stepwise feature selection, as e.g. described in Introduction to Statistical Learning, Chapter 6.

Here is some Python code from the chapter: https://github.com/JWarmenhoven/ISLR-python

You may also have a quick look at this post related to Lasso: https://datascience.stackexchange.com/a/55702/71442