  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.
