Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

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.