Predicting readmission risk after coronary artery bypass graft surgery using logistic regression model

Abstract

Abstract Coronary Artery Bypass Graft (CABG) post-operative readmission has a high incidence rate compared to other health cases. This particular case contributes to an increase in morbidity and hospital costs of patients. Therefore, an appropriate prediction model is needed while the model can be beneficial to the health financing institutions. There are many risk factors that will be used to predict CABG post-operative readmission. Of the many risk factors observed, some factors that have a significant influence on construction the Logistic Regression model will be determined. This model is developed to generate probabilities which are then called Created Readmission Risk Scores (CRRS).