Accurate identification of patients at high risk of in-hospital mortality in intensive care units (ICUs) is vital for enhancing clinical decision-making and improving patient care strategies. As traditional statistical models often fall short in modeling nonlinear and multifactorial clinical variables, this study explores a machine learning (ML) approach to overcome these limitations.

Our research performed a retrospective study using the MIMIC-IV database, focusing on 2,385 ICU patients who met predefined eligibility criteria. Numerical features were summarized through statistical aggregations (maximum, minimum, mean), while categorical attributes underwent structured encoding. The dataset was split into 70% for training and 30% for validation. We applied a combination of regularization techniques (LASSO, Ridge, ElasticNet) and Random Forest-based importance ranking for feature selection.

Multiple supervised ML algorithms, including CatBoost, XGBoost, and Support Vector Machines, were benchmarked using metrics such as AUC-ROC, calibration plots, and decision curve analysis. SHAP values were employed to enhance model explainability.

The CatBoost algorithm achieved the most favorable results with AUC scores of 0.904 and 0.868 on the training and test sets. These findings suggest that the proposed model offers a reliable, interpretable, and potentially integrable solution for ICU mortality risk prediction.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was conducted using the MIMIC-IV database, a de-identified and publicly available dataset. Ethical approval was not required. Access was granted under the credentialed data use agreement (Certification Number: 50778029). Patient consent was waived as the dataset used is fully de-identified and publicly available.

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Yes

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Emails: Yong Si: Email: yongsiusc.edu

Li Sun: Email: lsun4765usc.edu

Shuheng Chen: Email: shuhengcusc.edu

JunYi Fan: Email: junyifanusc.edu

Kamiar Alaei: Email: kamiar.alaeicsulb.edu

Elham Pishgar: Email: dr.elhampishgargmail.com

Greg Placencia: Emial: gvplacenciacpp.edu

Maryam Pishgar: Email: pishgarusc.edu