Authors : Francis Mawutor Amuyao; Isaac Tosin Adisa

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/2ksef8uk

Scribd : https://tinyurl.com/6rzwbbpw

DOI : https://doi.org/10.38124/ijisrt/26jul1334

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Machine learning (ML) models for sepsis mortality prediction are increasingly deployed in intensive care units, yet performance across demographic subgroups remains poorly evaluated, and algorithmic disparities can directly exacerbate health inequities in critical care settings. We trained three ML models—logistic regression (LR), XGBoost, and a multilayer perceptron (MLP)—on a 10,000-patient cohort calibrated to published MIMIC-IV sepsis statistics, and performed a four-metric fairness audit (equalized odds difference [EOD], demographic parity difference, predictive parity gap, and subgroup calibration error) across race/ethnicity, sex, and insurance type. Per-group threshold optimisation was applied for debiasing, and an accuracy–fairness Pareto tradeoff was quantified.

Keywords : Algorithmic Fairness; Sepsis Prediction; Machine Learning; MIMIC-IV; Equalized Odds; SHAP; Health Disparities; Debiasing; ICU.

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