Implementing an Ensemble Learning Model with Feature Selection to Predict Mortality among Patients Who Underwent Three-Vessel Percutaneous Coronary Intervention

Author:

Huang Yen-ChunORCID,Chen Kuan-Yu,Li Shao-Jung,Liu Chih-Kuang,Lin Yang-ChaoORCID,Chen MingchihORCID

Abstract

Coronary artery disease (CAD) is a common major disease. Revascularization with percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) could relieve symptoms and myocardial ischemia. As the treatment improves and evolves, the number of aged patients with complex diseases and multiple comorbidities gradually increases. Furthermore, in patients with multivessel disease, 3-vessel PCI may lead to a higher risk of complications during the procedure, leading to further ischemia and higher long-term mortality than PCI for one vessel or two vessels. Nevertheless, the risk factors for accurately predicting patient mortality after 3-vessel PCI are unclear. Thus, a new risk prediction model for primary PCI (PPCI) patients’ needs to be established to help physicians and patients make decisions more quickly and accurately. This research aimed to construct a prediction model and find which risk factors will affect mortality in 3-vessel PPCI patients. This nationwide population-based cohort study crossed multiple hospitals and selected 3-vessel PPCI patients from January 2007 to December 2009. Then five different single machine learning methods were applied to select significant predictors and implement ensemble models to predict the mortality rate. Of the 2337 patients who underwent 3-vessel PPCI, a total of 1188 (50.83%) survived and 1149 (49.17%) died. Age, congestive heart failure (CHF), and chronic renal failure (CRF) are mortality’s most important variables. When CRF patients accept 3-vessel PPCI at ages between 68–75, they will possibly have a 94% death rate; Furthermore, this study used the top 15 variables averaged by each machine learning method to make a prediction model, and the ensemble learning model can accurately predict the long-term survival of 3-vessel PPCI patients, the accurate predictions rate achieved in 88.7%. Prediction models can provide helpful information for the clinical physician and enhance clinical decision-making. Furthermore, it can help physicians quickly identify the risk features, design clinical trials, and allocate hospital resources effectively.

Funder

Ministry of Science and Technology

Fu Jen Catholic University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries;International Journal of Environmental Research and Public Health;2022-12-18

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