I have a dataset where I am working on a binary classification. I have two classes of subjects. One is Outpatients and Other is Inpatients. (66:33 is the class proportion)
My objective is to identify the risk factors that influence hospital admission (Inpatients).
But the problem here is, I have my dataset like as below
1) Let's say we have a subject called "John". He has visited hospital 19 times based on my data duration from Jan 2001- Dec 2005. All of his 19 visits are outpatients.
2) Let's say we have another subject called "Jack". He has visited the hospital 34 times based on data duration from Jan 2001-Dec 2005. Out of 34 visits, he has been admitted as inpatient 18 times and rest 16 are outpatient visits.
So now my question is
1) Usually for analysis, we only see one record per subject/individual. Right? But now on what basis should I pick that one record?
Meaning, for John out of his 19 visits, which one should I pick?
Similarly for Jack, out of his 18 inpatient visits, which one should I pick?
I choose only one out of 18 from Jack because we don't need his outpatient info as we already have a separate group of outpatients and jack is considered for Inpatient class (because he has inpatient records too unlike John).
2) Is it really necessary to have only one record per person for analysis? Is there anyway to do this? Or is it like I have to represent in aggregate form the info of multiple records in one record? Is there any theory that allows analysis of multiple records for an individual?
Hope my question is clear and kindly request you to help me