Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes. In this study, we explore the potential of utilizing data pre-processing techniques and the Extra-Tree (ET) feature selection method in conjunction with the Random Forest (RF) classifier to improve survival prediction in heart failure patients. By leveraging the strengths of ET feature selection, we aim to identify the most significant predictors associated with heart failure survival. Using the public UCL Heart failure (HF) survival dataset, we employ the ET feature selection algorithm to identify the most informative features. These features are then used as input for grid search of RF. Finally, the tuned RF Model was trained and evaluated using different matrices. The approach was achieved 98.33% accuracy that is the highest over the exiting work.

利用数据预处理技术和Extra-Tree特征选择方法结合Random Forest分类器，提高心衰患者生存预测准确性。采用UCL心衰生存数据集进行特征选择和调优，获得了98.33%的最高准确率。

发挥额外决策树特征选择和随机森林分类器的潜力，提高心力衰竭患者生存预测效果