Using Machine Learning and Electronic Health Record (EHR) Data for the Early Prediction of Alzheimer’s Disease and Related Dementias

Abstract

Objective: In the United States, over 6 million patients are affected by Alzheimer's Disease and Related Dementias (ADRD). The study aims to develop and validate machine learning (ML) models for the early diagnosis and prediction of ADRD using de-identified Electronic Health Record (EHR) data from the University of Missouri (MU) Healthcare for different prediction windows. Materials and Methods: The study used de-identified EHR data provided by the MU NextGen Biomedical Informatics (BMI). An initial cohort of 380,269 patients aged over 40 with at least two healthcare encounters was narrowed to a final dataset of 4,012 unique patients of ADRD cases and 119,723 unique patients of controls. We trained and evaluated six different ML classifier models: Gradient-Boosted Trees (GBT), Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient-Boosting (XGBoost), Logistic Regression (LR), and Adaptive Boosting (AdaBoost) using metrics such as Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score, accuracy, sensitivity, specificity, and F1 score. SHAP (SHapley Additive exPlanations) analysis was used to interpret predictions. Results: The GBT model achieved the best AUC-ROC scores of 0.809, 0.821, 0.822, 0.808, and 0.833 for 1-year, 2-year, 3-year, 4-year, and 5-year prediction windows, respectively. The SHAP analysis highlighted key risk factors for ADRD, including depressive disorder, heart disease, higher age, headache, anxiety, and insomnia. Conclusion: This study demonstrates the potential of ML models using EHR data for early ADRD prediction, enabling timely interventions to delay progression and improve outcomes. These findings offer insights for future research and proactive care strategies.