Abstract
ABSTRACT This study examines the combined use of machine learning (ML) and expert judgment in predicting 30-day mortality for congestive heart failure (CHF) patients. It compares models using either expert-selected, ML-selected, or integrated features. The integrated model, merging expert and ML insights, outperforms others in predicting mortality risk, underscoring the value of combining human expertise and ML in clinical decision-making.
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