The prediction of postoperative morbidity in coronary artery bypass grafting using Naïve Bayes Classification and Bayes Factor

A. Rachardi, D. Lestari, S. Mardiyati, S. Devila, A. H.A. Zili

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the healthcare system, Coronary Artery Bypass Grafting (CABG) surgery is performed to treat coronary artery disease. Complications of CABG postoperative disease, which is called postoperative morbidity, affect the length of stay of the patient in the hospital. Therefore, predict the risk of patients experiencing postoperative morbidity is required to optimize the use of resources in the hospital. This study proposed about predicting the postoperative morbidity from patients who underwent CABG based on risk factors by using Bayes Factor and Naïve Bayes Classification. The Bayes Factor method is applied to determine the risk factors that affect the prolonged hospital stay of patients postoperatively. Then the Naïve Bayes Classification is applied to predict postoperative morbidity, which leads the patient to be treated for more than 4 days. Data were obtained from medical history of patients at the FundaCardio Foundation in Venezuela during the period 2010-2014. Based on the results of the Bayes factor calculation, risk factors that influenced the length of stay diagnosis in the hospital include elderly, blood transfusion of more than 2 units of PRCB (Packet of Red Cell Blood), New York Heart Association (NYHA) heart failure classification, extubation process, duration of surgery and length of stay at the ICU. By using the Naïve Bayes Classification, it was found that these risk factors lead the patient to be hospitalized for more than 4 days. The results of this classification can be a consideration for patients in determining the cost of the coverage and the premium of an insurance product.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
EditorsTerry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420014
DOIs
Publication statusPublished - 1 Jun 2020
Event5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia
Duration: 9 Jul 201910 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2242
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
CountryIndonesia
CityDepok
Period9/07/1910/07/19

Keywords

  • Bayes factor
  • coronary artery bypass grafting
  • length of hospital stay
  • morbidity
  • nave bayes classification

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    Rachardi, A., Lestari, D., Mardiyati, S., Devila, S., & Zili, A. H. A. (2020). The prediction of postoperative morbidity in coronary artery bypass grafting using Naïve Bayes Classification and Bayes Factor. In T. Mart, D. Triyono, & T. A. Ivandini (Eds.), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 [030020] (AIP Conference Proceedings; Vol. 2242). American Institute of Physics Inc.. https://doi.org/10.1063/5.0008285