<ul><li>Accurate patient mortality prediction enables effective risk stratification, leading to personalized treatment plans and improved patient outcomes.</li><li>This study evaluates machine learning models for all-cause in-hospital mortality prediction using the MIMIC-III database, employing a comprehensive feature engineering approach.</li><li>The Random Forest model achieved the highest performance with an AUC of 0.94, significantly outperforming other machine learning and deep learning approaches.</li><li>The findings highlight the importance of careful feature engineering for accurate mortality prediction and propose future directions, including enhancing model robustness and tailoring prediction models for specific diseases.</li></ul>

Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data

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