The SME-XGBoost machine learning model effectively identified the risk of adverse outcomes in heart failure patients, achieving a mean AUC of 0.8010 (95% CI 0.7974-0.8046).
Observational (n=5,004)
Yes
The combination of SMOTE+ENN with XGBoost machine learning effectively improves the risk stratification and prediction of adverse outcomes in hospitalized heart failure patients.
Effect estimate: AUC 0.8010 (95% CI 0.7974-0.8046)
PURPOSE: This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. PATIENTS AND METHODS: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 cases were discharged after improvement. The study subjects were hospitalized patients diagnosed with HF from a regional cardiovascular hospital and the cardiology department of a medical university hospital in Shanxi Province of China between January 2014 and June 2019. Synthesizing minority oversampling technology combined with edited nearest neighbors (SMOTE+ENN) was used to pre-process unbalanced data. Traditional logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were used to build risk identification models, and each model was repeated 100 times. Model discrimination and calibration were estimated using F1-score, the area under the receiver-operating characteristic curve (AUROC), and Brier score. The best performing of the five models was used to identify the risk of adverse outcomes and evaluate the influencing factors. RESULTS: The SME-XGBoost was the best performing model with means of F1-score (0.3673, 95% confidence interval CI: 0.3633-0.3712), AUC (0.8010, CI: 0.7974-0.8046), and Brier score (0.1769, CI: 0.1748-0.1789). Age, N-terminal pronatriuretic peptide, pulmonary disease, etc. were the most significant factors of adverse outcomes in patients with HF. CONCLUSION: The combination of SMOTE+ENN and advanced machine learning methods effectively improved the discrimination efficacy of adverse outcomes in HF patients, accurately stratified patients at risk of adverse outcomes, and found the top factors of adverse outcomes. These models and factors emphasize the importance of health status data in determining adverse outcomes in patients with HF.
Wang et al. (Tue,) conducted a observational in Chronic Heart Failure (n=5,004). SME-XGBoost (SMOTE+ENN and XGBoost) vs. Traditional logistic regression, KNN, SVM, and random forest was evaluated on Risk identification of adverse outcomes (AUC 0.8010, 95% CI 0.7974-0.8046). The SME-XGBoost machine learning model effectively identified the risk of adverse outcomes in heart failure patients, achieving a mean AUC of 0.8010 (95% CI 0.7974-0.8046).