1
$\begingroup$

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?) 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?

Can you guide me on this?

$\endgroup$
1
$\begingroup$

1) I have two classes (Admitted & Not-admitted) 2) Around 25 input variables 3) Run a logistic regression (Statsmodel logit or Scikit-learn?) 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.

Not necessary, you can just perform clustering. Than remove features until classes are not distinct anymore. At this point the features that are left are the most important risk factors.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.