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Heart failure kills millions of people globally every year. This work employs a classification-based predictive system utilizing machine learning techniques and provides precise health analysis. The model is evaluated based on the F1 score, accuracy, precision, and confusion matrix. We have obtained an accuracy of 88.33 percent by tuning the appropriate hyperparameter. This research aims to identify the significance of hyperparameter tuning within classification algorithms. The result of this study may potentially make significant changes in the healthcare sector and may also increase the chances of survival of heart failure patients. This study also tries to identify the important features that should be considered while analyzing heart failure survival based on the publicly available dataset. The model has been made easily accessible over a web-based UI interface.
Sandilya et al. (Thu,) studied this question.