TY - GEN
T1 - The prediction of postoperative morbidity in coronary artery bypass grafting using Naïve Bayes Classification and Bayes Factor
AU - Rachardi, A.
AU - Lestari, D.
AU - Mardiyati, S.
AU - Devila, S.
AU - Zili, A. H.A.
N1 - Funding Information:
The author send the gratitude to the DPRM Universitas Indonesia and the DPRM FMIPA Universitas Indonesia for the funding assistance into this research through the PITTA 2019 research grant.
Publisher Copyright:
© 2020 Author(s).
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - Bayes factor
KW - coronary artery bypass grafting
KW - length of hospital stay
KW - morbidity
KW - nave bayes classification
UR - http://www.scopus.com/inward/record.url?scp=85086659196&partnerID=8YFLogxK
U2 - 10.1063/5.0008285
DO - 10.1063/5.0008285
M3 - Conference contribution
AN - SCOPUS:85086659196
T3 - AIP Conference Proceedings
BT - Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
A2 - Mart, Terry
A2 - Triyono, Djoko
A2 - Ivandini, Tribidasari Anggraningrum
PB - American Institute of Physics Inc.
T2 - 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
Y2 - 9 July 2019 through 10 July 2019
ER -