A Comprehensive Review on Predicting Heart Failure with Ensemble Learning: Integrating Environmental Variables and Health Perspectives

Abstract

In this paper, authors perform a review of the prediction of HF using ensemble ML models, considering environmental and health variables. These ensemble ML models can be more accurate methodologies than single model algorithms. This study summarises and combines the research related to well-known ensemble methods like: AdaBoost, XGBoost, CatBoost, Random Forest, stacking and voting classifiers. Of these, stacking and voting methods were always superior to the others in terms of predicting HF risk, and both achieved relatively good accuracy and stable performance in different datasets. It also discusses recent alternative approaches such as Least Error Boosting, BOO-ST, and CBCEC which deal with multiple issues such as data imbalance and feature selection. The majority of the models had predictive accuracies > 90%, indicating their applicability. However, the review finds long-existing problems, including data quality, model interpretability, and class imbalance. It appeals for further investigation into alternative approaches, namely quantum machine learning and transfer learning that can address these challenges. This study highlights the clinical value of such robust ensemble ML models for the early detection of HF enabling intervention planning, and improved patient outcomes, perhaps as part of a decision support system designed to revolutionize heart failure management in healthcare.