Key result
Stacking ensemble ML achieves ~90% accuracy predicting heart failure risk, outperforming standalone XGBoost.
Why the study?
Cardiac disorders claim approximately 17.9 million lives annually, and identifying at-risk patients before clinical deterioration offers substantial therapeutic benefit through prompt intervention.
Does a Stacking Ensemble machine learning model improve binary classification accuracy of heart failure risk compared to a standalone XGBoost classifier?
Does a Stacking Ensemble machine learning model improve binary classification accuracy of heart failure risk compared to a standalone XGBoost classifier?
A Stacking Ensemble machine learning approach achieved approximately 90% accuracy in predicting heart failure risk, demonstrating potential as an automated clinical decision support tool.
Among non-communicable diseases, cardiac disorders account for the highest share of global deaths, claiming roughly 17.9 million lives annually. Identifying at-risk patients before clinical deterioration offers substantial therapeutic benefit, as prompt intervention materially changes prognosis. This study constructs a machine learning pipeline for binary classification of heart failure risk from structured patient data. Two architectures are developed and compared: an XGBoost classifier and a Stacking Ensemble that combines Random Forest and Gradient Boosting as base estimators, with Logistic Regression serving as the second-level meta-learner. Both models are trained and evaluated on the Heart Failure Prediction Dataset, an aggregated repository of 918 records characterised by 11 physiological attributes. The Stacking Ensemble attains approximately 90% classification accuracy, outperforming the standalone baseline. A Flask web interface exposes the trained model for real-time clinical scoring. The results underscore the value of ensemble strategies in building automated decision support tools for cardiac care.
No takes yet. Share an insight, caveat, or question.
Das et al. (2026) studied Heart failure risk (n=918). Stacking Ensemble machine learning model vs. XGBoost classifier was evaluated on Classification accuracy. A Stacking Ensemble machine learning model achieved approximately 90% classification accuracy for predicting heart failure risk, outperforming a standalone XGBoost baseline.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: