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Updated: Jul 29, 2026
Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Red-flagging multimorbidity clusters for Alzheimer's disease risk using explainable machine learning: Evidence from a
Tursun Alkam1, Ebrahim Tarshizi1, Andrew H Van Benschoten1
1Master's Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA, USA.
Background:
Emergency-department (ED) visits capture diagnostic data that could flag patients at heightened risk for Alzheimer's disease (AD) long before cognitive symptoms are formally recognized.
Objective:
To examine how age and multimorbidity interact to predict AD and to test whether explainable machine learning enhances risk stratification using national ED data.
Methods:
We analyzed 554,985 ED visits (2010-2014 National Emergency Department Sample) from adults ≥ 60 y. ICD-9-CM codes identified AD and 17 chronic conditions. Logistic regression estimated odds ratios (ORs) for single and combined comorbidities across five age bands. Predictive performance of logistic regression, decision tree, random forest and XGBoost was compared; Shapley Additive exPlanations (SHAP) interpreted model output.
Results:
Urinary-tract infection (UTI; OR = 2.74), depression (1.93), hypothyroidism (1.68) and anemia (1.57) independently increased AD odds. Possessing all four "red-flag" conditions tripled risk (OR = 3.31), and each additional red-flag raised risk by 74%. Age showed a steep gradient: relative to 60-65 y, ORs climbed from 2.58 (66-70 y) to 25.8 (86-90 y; all p < 0.001). XGBoost performed best (AUC = 0.782; recall = 0.821), and SHAP confirmed age and red-flag multimorbidity as dominant predictors.
Conclusions:
Routine ED codes reveal an age-dependent, dose-response relationship between specific multimorbidity clusters and AD. An interpretable XGBoost model accurately identifies high-risk patients, outlining a practical pathway for real-time cognitive-risk alerts in acute-care settings.
