Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection
Abstract Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural compl...
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BMC
2025-01-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-025-12218-6 |
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| author | Kuan-Yu Chen Yen-Chun Huang Chih-Kuang Liu Shao-Jung Li Mingchih Chen |
| author_facet | Kuan-Yu Chen Yen-Chun Huang Chih-Kuang Liu Shao-Jung Li Mingchih Chen |
| author_sort | Kuan-Yu Chen |
| collection | DOAJ |
| description | Abstract Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural complications, which can increase healthcare costs. Developing efficient strategies for resource allocation has become a paramount concern due to tightening healthcare budgets and the escalating costs of treating heart conditions. Therefore, it is essential to develop an evaluation model to estimate the costs of PCI surgeries and identify the key factors influencing these costs to enhance healthcare quality. This study utilized the National Health Insurance Research Database (NHIRD), encompassing data from multiple hospitals across Taiwan and covering up to 99% of the population. The study examined data from triple-vessel PCI patients treated between January 2015 and December 2017. Additionally, six machine-learning algorithms and five cross-validation techniques were employed to identify key features and construct the evaluation model. The machine learning algorithms used included linear regression (LR), random forest (RF), support vector regression (SVR), generalized linear model boost (GLMBoost), Bayesian generalized linear model (BayesGLM), and extreme gradient boosting (eXGB). Among these, the eXGB model exhibited outstanding performance, with the following metrics: MSE (0.02419), RMSE (0.15552), and MAPE (0.00755). We found that the patient’s medication use in the previous year is also crucial in determining subsequent surgical costs. Additionally, 25 significant features influencing surgical expenses were identified. The top variables included 1-year medical expenditure before PCI surgery (hospitalization and outpatient costs), average blood transfusion volume, ventilator use duration, Charlson Comorbidity Index scores, emergency department visits, and patient age. This research is crucial for estimating potential expenses linked to complications from the procedure, directing the allocation of resources in the future, and acting as an important resource for crafting medical management policies. |
| format | Article |
| id | doaj-art-1eab2cddb0764dd8acebe73ab6bf63ec |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-1eab2cddb0764dd8acebe73ab6bf63ec2025-01-26T12:21:54ZengBMCBMC Health Services Research1472-69632025-01-0125111710.1186/s12913-025-12218-6Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selectionKuan-Yu Chen0Yen-Chun Huang1Chih-Kuang Liu2Shao-Jung Li3Mingchih Chen4Division of Cardiology, Taipei City Hospital, Zhongxing BranchDepartment of Artificial Intelligence, Tamkang UniversityArtificial Intelligence Development Center, Fu Jen Catholic UniversityCardiovascular Research Center, Wan Fang Hospital, Taipei Medical UniversityGraduate Institute of Business Administration, College of Management, Fu Jen Catholic UniversityAbstract Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural complications, which can increase healthcare costs. Developing efficient strategies for resource allocation has become a paramount concern due to tightening healthcare budgets and the escalating costs of treating heart conditions. Therefore, it is essential to develop an evaluation model to estimate the costs of PCI surgeries and identify the key factors influencing these costs to enhance healthcare quality. This study utilized the National Health Insurance Research Database (NHIRD), encompassing data from multiple hospitals across Taiwan and covering up to 99% of the population. The study examined data from triple-vessel PCI patients treated between January 2015 and December 2017. Additionally, six machine-learning algorithms and five cross-validation techniques were employed to identify key features and construct the evaluation model. The machine learning algorithms used included linear regression (LR), random forest (RF), support vector regression (SVR), generalized linear model boost (GLMBoost), Bayesian generalized linear model (BayesGLM), and extreme gradient boosting (eXGB). Among these, the eXGB model exhibited outstanding performance, with the following metrics: MSE (0.02419), RMSE (0.15552), and MAPE (0.00755). We found that the patient’s medication use in the previous year is also crucial in determining subsequent surgical costs. Additionally, 25 significant features influencing surgical expenses were identified. The top variables included 1-year medical expenditure before PCI surgery (hospitalization and outpatient costs), average blood transfusion volume, ventilator use duration, Charlson Comorbidity Index scores, emergency department visits, and patient age. This research is crucial for estimating potential expenses linked to complications from the procedure, directing the allocation of resources in the future, and acting as an important resource for crafting medical management policies.https://doi.org/10.1186/s12913-025-12218-6PCINational Health Insurance Research Database; NHIRDMedical expenseMachine learning methodsFeature selection |
| spellingShingle | Kuan-Yu Chen Yen-Chun Huang Chih-Kuang Liu Shao-Jung Li Mingchih Chen Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection BMC Health Services Research PCI National Health Insurance Research Database; NHIRD Medical expense Machine learning methods Feature selection |
| title | Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection |
| title_full | Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection |
| title_fullStr | Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection |
| title_full_unstemmed | Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection |
| title_short | Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection |
| title_sort | machine learning driven prediction of medical expenses in triple vessel pci patients using feature selection |
| topic | PCI National Health Insurance Research Database; NHIRD Medical expense Machine learning methods Feature selection |
| url | https://doi.org/10.1186/s12913-025-12218-6 |
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