Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm

TL;DR

This study develops a stacked ensemble machine learning model that accurately predicts heart failure patient survival, demonstrating the effectiveness of supervised algorithms and key patient attributes in healthcare decision-making.

Contribution

The paper introduces a novel stacked ensemble learning approach for heart failure survival prediction, outperforming individual models with near-perfect accuracy.

Findings

Supervised ML models outperform unsupervised clustering in prediction accuracy.

The ensemble model achieves 99.98% accuracy and high precision, recall, F1 score.

Key patient attributes are identified as critical for accurate survival prediction.

Abstract

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.