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Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
 
 
Article

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

by 1,2, 3,4,5,6, 1,2,* and 1,2,*
1
Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan
2
Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
3
Cardiovascular Research Center, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan
4
Taipei Heart Institute, Taipei Medical University, New Taipei City 231, Taiwan
5
Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 116, Taiwan
6
Division of Cardiovascular Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei City 116, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editor: Mahmudur Rahman
Healthcare 2021, 9(6), 710; https://doi.org/10.3390/healthcare9060710
Received: 24 May 2021 / Revised: 7 June 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future. View Full-Text
Keywords: National Health Insurance Research Database; NHIRD; CABG; machine learning; medical expenditure predict; feature selection National Health Insurance Research Database; NHIRD; CABG; machine learning; medical expenditure predict; feature selection
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MDPI and ACS Style

Huang, Y.-C.; Li, S.-J.; Chen, M.; Lee, T.-S. The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients. Healthcare 2021, 9, 710. https://doi.org/10.3390/healthcare9060710

AMA Style

Huang Y-C, Li S-J, Chen M, Lee T-S. The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients. Healthcare. 2021; 9(6):710. https://doi.org/10.3390/healthcare9060710

Chicago/Turabian Style

Huang, Yen-Chun, Shao-Jung Li, Mingchih Chen, and Tian-Shyug Lee. 2021. "The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients" Healthcare 9, no. 6: 710. https://doi.org/10.3390/healthcare9060710

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