A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure

Objective: To identify risk factors for first-attempt failure of radial arterial puncture in heart failure patients and to develop and compare predictive models using logistic regression with advanced feature engineering and ensemble learning approaches.Method: A retrospective study was conducted involving 789 heart failure patients who underwent radial arterial puncture. Patients were divided into a training set (80%) and a test set (20%) using a stratified hold-out method. A logistic regression framework incorporating feature engineering (interaction terms, transformations, composite scores, PCA) and a dual-stage variable selection strategy (LASSO followed by stepwise selection with p