Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood, indicating the need for more accurate prediction models. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes. Preprocessing steps included handling missing data, removing duplicates, treating skewness, and using oversampling techniques to address data imbalances. Through rigorous feature selection using Variance Inflation Factor (VIF), expert clinical input, and ablation studies, 46 key features were identified to enhance model performance. Our analysis compared several machine learning models, including Logistic Regression, Support Vector Machine (SVM), Random Forest, LightGBM, and XGBoost. XGBoost emerged as the superior model, achieving a test AUC-ROC of 0.9228 (95\% CI 0.8748 - 0.9613), significantly outperforming our previous work (AUC-ROC of 0.8766) and the best results reported in existing literature (AUC-ROC of 0.824). The improved model's success is attributed to advanced feature selection methods, robust preprocessing techniques, and comprehensive hyperparameter optimization through Grid-Search. SHAP analysis and feature importance evaluations based on XGBoost highlighted key variables like leucocyte count and RDW, providing valuable insights into the clinical factors influencing mortality risk. This framework offers significant support for clinicians, enabling them to identify high-risk ICU heart failure patients and improve patient outcomes through timely and informed interventions.

本研究解决了ICU心力衰竭患者死亡率预测不准确的问题。通过分析MIMIC-III数据库中1,177名患者的数据，采用了先进的特征选择方法和机器学习模型比较，发现XGBoost模型在AUC-ROC测试中表现优异，达到了0.9228，为临床提供了识别高风险患者的有效工具，潜在提高护理结果。

优化ICU心力衰竭患者的死亡率预测：利用XGBoost和先进的机器学习与MIMIC-III数据库