Introduction: The American Heart Association Get With the Guidelines - Coronary Artery Disease (GWTG-CAD) Registry is a quality improvement program with over 2600 participating U.S. hospitals. Among the collected data elements is the yes-no variable “Heart failure documented on First Medical Contact” (HF-FMC), an important clinical predictor of in-hospital mortality. Before 2018, the missing rate of HF-FMC exceeded 60% in some years, significantly limiting its utility as a predictor of in-hospitality mortality. We compared machine learning (ML)-based with traditional statistical methods to accurately impute missing HF-FMC values. Methods: We utilized admission records from the GWTG CAD Registry data from 2018 to 2021, including both STEMI and NSTEMI patients. We removed missing HF-FMC, using complete data as ground truth. The data was split into training and test datasets in a 3:1 ratio. Imputation was framed as a prediction task. For evaluation, we masked HF-FMC in the test set and compared the imputed values against the ground truth. For traditional methods, mode imputation, K-nearest neighbor imputer (KNN), and Multiple Imputation by Chained Equations (MICE) were applied. For ML models, we applied logistic regression, random forest and XGBoost. Shapley Additive exPlanations (SHAP) values were utilized to identify key features contributing to the ML models. Results: The study included 223,074 individuals (12.2% HF-FMC = “yes”), with mean (SD) age 64.9 (13.4) years (33.8% Female; and 77.3% White and 13.8% Black). Machine Learning models achieved higher AUROC scores compared to traditional models in imputing HF-FMC (Table 1). Top features for predicting HF-FMC are shown (Figure 1). Conclusions: Machine learning models outperformed traditional methods in imputing missing HF-FMC, a critical predictor of in-hospital mortality. SHAP values can identify key features in imputing clinically important variables with high degree of missingness.