Different strategies for dealing with features with multiple values per sample in python machine learning models
You should make the &quot;abdomCirc&quot; as Multiple separate features (e.g. one for each month).
 Then handle NaN as it should be i.e.

Remove columns that are having more NaN than a Threshold
Try to fill NaN for cases where only a few are missing.

If we can't accommodate the above solution because of NaN counts, then we should accept that we don't have enough data and ignore the feature-set (i.e. abdomCirc) altogether.
Otherwise you may be at risk of finding a pattern that doesn't exist because of very small amount of available data points for abdomCirc.

I initially thought about using a python list for these features. However, I do not know if a machine learning model can handle this data type

None of the Model will accept this data i.e. a List