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

ABSTRACT: Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning method

SUBMITTER: Huang YC 

PROVIDER: S-EPMC8151160 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature