Handling features with multiple values per instance in Python for Machine Learning model

I have a dataset which contains medical data about children and I am developing a predictive machine learning model to predict adverse pregnancy outcomes. The dataset contains mostly features with a single value per child, e.g. gender = ["Male", "Female].
However, I have some features that have multiple values per child, such as the abdominal circumference which has been recorded multiple times per child, as such:

    ChildID     abdomcirc
0   1           273
1   1           267
2   1           294
3   2           136
4   2           248

So in the above table child 1 has 3 values for abdomcirc and child 2 has two values for abdomcirc. Adding this feature to the remaining dataset (comprised of single observational features) will result in nearly duplicate rows, apart from the different values for abdomcirc, like so:

    ChildID     gender  diabetes  birthroute  abdomcirc
0   1           Male    No        Normal      273
1   1           Male    No        Normal      267
2   1           Male    No        Normal      294
3   2           Female  Yes       csection    136
4   2           Female  Yes       csection    248

I am unsure what the best way to deal with these features is, without merging the data and having near-duplicate rows. I have considered the following:

• Using python list type for abdomcirc. However, I do not know if a machine learning model can handle this data type. So my data will look something like this

        ChildID     gender  diabetes  birthroute  abdomcirc
   0    1           Male    No        Normal      [273, 267, 294]
   1    2           Female  Yes       csection    [136, 248]
  

• Transforming abdomcirc into a single observational feature by calculating the mean (although I am not sure how useful this information would be for my predictive model) like so:

        ChildID     gender  diabetes  birthroute  abdomcirc
   0    1           Male    No        Normal      278
   1    2           Female  Yes       csection    192
  

I have tried looking for resources to help me with this but have not been very successful, maybe because I am not typing the correct keywords or something. So, I would appreciate your opinions and helpful resources. Many thanks!