College
Buchtel College of Arts and Sciences
Date of Last Revision
2026-05-07 06:09:15
Major
Statistics
Honors Course
STAT 498
Number of Credits
3
Degree Name
Bachelor of Science
Date of Expected Graduation
Spring 2026
Abstract
Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, CART decision tree, random forest, XGBoost, and LACE index models were tuned and evaluated using the area under the receiver operator characteristic curve (AUC-ROC) metric, which measures discrimination across all decision thresholds. These findings suggest that the four machine learning architectures provide a notable lift above the LACE index (AUC 0.6471), with the XGBoost model (AUC 0.7310) achieving the highest discriminatory ability. Nonparametric ensemble methods, such as the random forest and XGBoost architectures, excel at modeling complex interactions between an array of high-dimensional predictors. To address the “black box” nature of these models, SHAP values were leveraged to provide insight into model logic and interpret prediction contributions. This research explores how modern machine learning methodologies, paired with explainable AI tools, can bridge the traditional divide between predictive power, actionable insight, and clinical trust.
Research Sponsor
Dr. Nao Mimoto
First Reader
Dr. Mark Fridline
Second Reader
Dr. Jun Ye
Honors Faculty Advisor
Dr. Nao Mimoto
Proprietary and/or Confidential Information
No
Recommended Citation
Hughes, Carl E. III, "Beyond the LACE Index: Benchmarking Machine Learning Architectures and Explaining 30-Day Hospital Readmission Risk with SHAP Analysis" (2026). Williams Honors College, Honors Research Projects. 2194.
https://ideaexchange.uakron.edu/honors_research_projects/2194
Included in
Applied Statistics Commons, Biostatistics Commons, Categorical Data Analysis Commons, Databases and Information Systems Commons, Data Science Commons, Probability Commons, Statistical Models Commons