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This paper uses the MIMIC-IV dataset to examine the fairness and bias in anXGBoost binary classification model predicting the Intensive Care Unit (ICU)length of stay (LOS). Highlighting the critical role of the ICU in managingcritically ill patients, the study addresses the growing strain on ICUcapacity. It emphasizes the significance of LOS prediction for resourceallocation. The research reveals class imbalances in the dataset acrossdemographic attributes and employs data preprocessing and feature extraction.While the XGBoost model performs well overall, disparities across race andinsurance attributes reflect the need for tailored assessments and continuousmonitoring. The paper concludes with recommendations for fairness-aware machinelearning techniques for mitigating biases and the need for collaborativeefforts among healthcare professionals and data scientists.

Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction