LeonardoSaccotelli/Cost-Sensitive-Learning-For-Myocardial-Infarction-Mortality-Prediction
This study presents a comparative analysis of static and dynamic ensemble learning for predicting myocardial infarction mortality. To mitigate the challenges of class imbalance, the research implements a comprehensive cost-sensitive framework that operates at the data, algorithmic, and evaluation levels.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 16, 2026
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