This poster will be displayed in conjunction with the HIMSS20 Career Expo. Posters will be available for viewing through the entirety of the event. Poster presenters will be available for questions from 12:00 pm -1:00 pm.
Most datasets are highly skewed and suffer from class imbalance. Typically, positive class (e.g. diagnoses, mortality, fraud) constitutes a very small minority of the predicted feature. Consequently, the trained ML model using such data are going to be highly biased toward the majority class, perform poorly and exhibit high false negative rate. Imbalanced data prevail in most of the fields including Healthcare. Various techniques have been developed to balance the classes. However, in the healthcare informatics paradigm, little attention has been drawn to compare the performances of these techniques while developing a novel model, such as 30 day hospital readmission prediction model. This research aims to compare the ML models performances of six different balancing techniques for a 30-day hospital readmission prediction task.