  Cardiovascular disease, especially heart failure is one of the major health
hazard issues of our time and is a leading cause of death worldwide.
Advancement in data mining techniques using machine learning (ML) models is
paving promising prediction approaches. Data mining is the process of
converting massive volumes of raw data created by the healthcare institutions
into meaningful information that can aid in making predictions and crucial
decisions. Collecting various follow-up data from patients who have had heart
failures, analyzing those data, and utilizing several ML models to predict the
survival possibility of cardiovascular patients is the key aim of this study.
Due to the imbalance of the classes in the dataset, Synthetic Minority
Oversampling Technique (SMOTE) has been implemented. Two unsupervised models
(K-Means and Fuzzy C-Means clustering) and three supervised classifiers (Random
Forest, XGBoost and Decision Tree) have been used in our study. After thorough
investigation, our results demonstrate a superior performance of the supervised
ML algorithms over unsupervised models. Moreover, we designed and propose a
supervised stacked ensemble learning model that can achieve an accuracy,
precision, recall and F1 score of 99.98%. Our study shows that only certain
attributes collected from the patients are imperative to successfully predict
the surviving possibility post heart failure, using supervised ML algorithms.
