Background: I just started with machine learning and I'm considering using it on old data based on which I'm writing a paper. The paper deals with radiation-induced lung damage and the data comprise breathing rate measurements, as well as different types of histological scorings for each animal.
One of the questions I would like to answer in the paper is if the histology is a predictor of the lung damage, and if yes, which feature of the histology is the most important predictor. The outcome "lung damage" is a boolean value based on the breathing frequency.
My idea was to let a random forest predict the outcome using the scoring data and report the important variables in the paper with the statement that "Scorings of the histological features X, Y and Z are the best predictors for lung damage". IMO using machine learning to do that would give me a qualitative measure of important variables, but would spare me the task of developing a complicated model myself which predicts the outcome, as this is not the main point of the paper.
My questions are:
- Is this possible and a good idea?
- Is the variable importance a robust measure, or will slight changes in the data lead to an entirely different variable importance ranking?
- Is the accuracy of the prediction important in this context and how accurate must the prediction be in order for me to be sure that the variable importance is right?
Thank you very much for your insights!