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Machine Learning Prediction of One-Year Rehospitalization in Dementia: A Retrospective Cohort Study
Amirreza Esmaeili1, Vahid Saeedi2, Leila Kamalzadeh3
1Department of Clinical Health Psychology, School of Behavioral Science and Mental Health Iran University of Medical Sciences Tehran Iran.
Background And Aims:
Patients with dementia have high rehospitalization rates, creating substantial clinical and economic burden. Machine learning (ML) may help predict readmission risk, but its use for clinically relevant outcomes such as rehospitalization in dementia remains limited. This study evaluated the performance of several ML algorithms for predicting 1-year rehospitalization in patients with dementia and identified key predictive factors.
Methods:
In this retrospective cohort study, data from 606 patients with dementia admitted to a tertiary hospital in Tehran, Iran, between 2016 and 2024 were analyzed. Four ML algorithms-Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost)-were used to predict rehospitalization within 1 year of discharge. Predictors included demographic variables, dementia subtype, clinician-assigned functional severity stage based on DSM-5 criteria, behavioral disturbances, medical and psychiatric comorbidities, number of medications, and length of stay. Model performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) with fivefold cross-validation. Feature importance analysis was performed to identify the main predictors.
Results:
Of 606 patients, 232 (38.3%) were rehospitalized within 1 year. All models showed strong predictive performance. RF achieved the highest test accuracy (0.929) and AUC-ROC (0.986), followed by XGBoost (accuracy 0.918, AUC-ROC 0.982). LR and DT also performed well (LR: accuracy 0.896, AUC-ROC 0.964; DT: accuracy 0.890, AUC-ROC 0.965). Feature importance analysis identified functional severity stage, behavioral disturbance, polypharmacy, medical comorbidity burden, and psychiatric comorbidity as major predictors.
Conclusions:
ML models, particularly RF and XGBoost, showed strong performance in predicting 1-year rehospitalization among patients with dementia. Functional severity stage and behavioral disturbances were among the most important predictors. These findings support the potential use of ML for risk stratification and targeted intervention, although external validation in multicenter settings is needed.