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This is a Quantitative Study study.

October 22, 2025Open Access

Fairness-Aware Machine Learning for Heart Failure Prediction: Performance, Bias, and Clinical Deployment Insights

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Machine learning approaches improve heart failure prediction accuracy, suggesting the need for fairness in models.

Machine learning models for heart failure showed varying predictive performance based on gender and data source.
The analysis revealed a notable AUC-ROC of 0.986 for Transformer models, though their generalizability was lacking.
Ensemble methods can reduce bias in heart failure predictions, emphasizing the importance of fairness in healthcare.
Strategies like gender-specific threshold optimization are essential for deploying effective, equitable prediction tools.

Eziama et al. (2025) studied this question.

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October 22, 2025Open Access

View Full Paper

Ask AI

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Authors

Machine learning approaches improve heart failure prediction accuracy, suggesting the need for fairness in models.

Machine learning models for heart failure showed varying predictive performance based on gender and data source.
The analysis revealed a notable AUC-ROC of 0.986 for Transformer models, though their generalizability was lacking.
Ensemble methods can reduce bias in heart failure predictions, emphasizing the importance of fairness in healthcare.
Strategies like gender-specific threshold optimization are essential for deploying effective, equitable prediction tools.

Eziama et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark