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A Machine Learning Framework for Predicting 30-Day Hospital Readmissions in the United States: Socioeconomic, Clinical, and Policy Implications

by Chidinma Queen Adieze, Elo-Oghene Imonifano, Oluchi Uzoaru Anyom
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 131
Year of Publication: 2026
Authors: Chidinma Queen Adieze, Elo-Oghene Imonifano, Oluchi Uzoaru Anyom
10.5120/ijcaf78b222ffdd5

Chidinma Queen Adieze, Elo-Oghene Imonifano, Oluchi Uzoaru Anyom . A Machine Learning Framework for Predicting 30-Day Hospital Readmissions in the United States: Socioeconomic, Clinical, and Policy Implications. International Journal of Computer Applications. 187, 131 ( Aug 2026), 18-31. DOI=10.5120/ijcaf78b222ffdd5

@article{ 10.5120/ijcaf78b222ffdd5,
author = { Chidinma Queen Adieze, Elo-Oghene Imonifano, Oluchi Uzoaru Anyom },
title = { A Machine Learning Framework for Predicting 30-Day Hospital Readmissions in the United States: Socioeconomic, Clinical, and Policy Implications },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 131 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 18-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number131/a-machine-learning-framework-for-predicting-30-day-hospital-readmissions-in-the-united-states-socioeconomic-clinical-and-policy-implications/ },
doi = { 10.5120/ijcaf78b222ffdd5 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:54:29.173649+05:30
%A Chidinma Queen Adieze
%A Elo-Oghene Imonifano
%A Oluchi Uzoaru Anyom
%T A Machine Learning Framework for Predicting 30-Day Hospital Readmissions in the United States: Socioeconomic, Clinical, and Policy Implications
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 131
%P 18-31
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hospital readmissions within 30 days remain a major quality and cost burden in the United States, costing Medicare more than $26 billion annually and triggering financial penalties under the Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program (HRRP). Traditional risk-adjustment models insufficiently account for social determinants of health (SDOH), potentially reinforcing inequities. This study developed an interpretable machine learning framework using 4.8 million Medicare fee-for-service discharges (2019–2022), linked with socioeconomic indicators, to predict 30-day all-cause readmissions and assess disparities. Five models were compared, with XGBoost incorporating SDOH achieving the highest performance (AUC = 0.871), outperforming both clinical-only models and the LACE+ baseline. Inclusion of SDOH variablessuch as area-level poverty, dual eligibility, and deprivation index improved predictive accuracy (ΔAUC = 0.024). SHAP analysis identified prior hospitalizations, length of stay, and comorbidity burden as the strongest predictors. However, lower model performance for Black and Hispanic patients and higher readmission rates in safety-net hospitals highlight persistent racial and socioeconomic disparities. These findings support integrating SDOH into equity-aware risk adjustment frameworks to improve fairness and policy effectiveness under HRRP.

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Index Terms

Computer Science
Information Sciences

Keywords

Hospital readmissions; machine learning; social determinants of health; Medicare; health equity

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