I have a dataset of patients who have been inpatients (admitted to hospital) and not admitted (but visited as outpatients). Class proportion is 66:34.
I have collected a list of features for all these patients.
Now my objective is to find/identify the risk factors that leads to hospital admission? Meaning what are the risk factors that can influence a patient to be admitted? How the risk factors are different between two classes? For example patient with High heart rate or some sensitive clinical parameter(just example) could get admitted whereas person with normal clinical paramters may not get admitted but visit only for consulation.
Can you confirm whether my steps below are right?
1) I have two classes (Admitted & Not-admitted)
2) Around 25 input variables
3) Run a logistic regression (Statsmodel logit or Scikit-learn?)
4) Do we always have to predict the outcome class to know the risk factors that lead to admission/hospitalization?
5) Then identify the significant risk factors based on p-value.
Though my objective is to identify the risk factors that leads to hospital admission, do I still have to predict the outcome class to know the risk factors?
Since this sounds like an approach for binary classification, how do I know that the risk factors that I get is only for "hospital admission class"? Does it mean the risk factors are always only for one class (which I choose to set as 1 (hospital admission)?