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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 1) The data imbalance and 2) 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..

Any ideas will be appreciated.

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  • $\begingroup$ Have a read of Frank Harrell's regression modelling strategies book. It will give you since useful pointers and ideas. Your task is pretty challenging given how little data you really have; any signal would have to be amazingly strong. There is a feature importance for LR (incl. for LASSO/elastic net etc.), but you also need too think about within patient correlation (perhaps longitudinal joint models are something you need to consider?). $\endgroup$ – Björn Apr 2 at 4:00

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