I have a project which I am just starting out, I am only just learning machine learning and statistics so I am somewhat unsure as to what approaches will be good to start off with, and I am sorry if this does not belong here.
The data set is of different patients carrying a certain disease and each patient has different biomarkers and physical measurements such as heart rate at different time points, until death, if they do die. I was told that the goal was to identify the key features, which would be associated with a a patient dying.
I only have 33 patients, and only 16 of them have died. But disregarding patient the biomarkers came from I have 300 odd time slots, I first tried to approach it as a binary classification problem, classifying the 'death' point from other points. The problems were:
- The data imbalance and,
- How to you interpret the models to discover most important features.
For imbalance, I tried SMOTE oversampling with didn't work as I thought, then I randomly under-sampled, which gave decent results but the data set was even smaller, so I wasn't sure if its a good idea.
Simple binary classification models like Gaussian Naive Bayes and Logistic Regression did okay even with the imbalanced data, but they don't (at least as far as I know) give a way to discern feature importance..
So my main questions are:
What's the best way to approach this problem, or in general what kind of approaches work when you want to identify most influential features (data measurements).
If I do want to approach it as a binary classification problem what approaches can I take to combat class imbalance?