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

V. Febriani, D. Lestari, S. Mardiyati, S. Devila

Research output: Contribution to journalConference articlepeer-review

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).

Original languageEnglish
Article number012083
JournalJournal of Physics: Conference Series
Volume1725
Issue number1
DOIs
Publication statusPublished - 12 Jan 2021
Event2nd Basic and Applied Sciences Interdisciplinary Conference 2018, BASIC 2018 - Depok, Indonesia
Duration: 3 Aug 20184 Aug 2018

Keywords

  • Morbidity
  • Risk factors
  • Wald tests
  • Weighted least square

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