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Heart failure is a life-threatening condition that affects millions of peopleworldwide. The ability to accurately predict patient survival can aid in earlyintervention and improve patient outcomes. In this study, we explore thepotential of utilizing data pre-processing techniques and the Extra-Tree (ET)feature selection method in conjunction with the Random Forest (RF) classifierto improve survival prediction in heart failure patients. By leveraging thestrengths of ET feature selection, we aim to identify the most significantpredictors associated with heart failure survival. Using the public UCL Heartfailure (HF) survival dataset, we employ the ET feature selection algorithm toidentify the most informative features. These features are then used as inputfor grid search of RF. Finally, the tuned RF Model was trained and evaluatedusing different matrices. The approach was achieved 98.33% accuracy that is thehighest over the exiting work.

Unleashing the Power of Extra-Tree Feature Selection and Random Forest Classifier for Improved Survival Prediction in Heart Failure Patients