Prediction Of Alzheimer's Disease Using Health Records and Machine Learning Based Framework
Keywords:
Alzheimer’s Disease, ADRD, Machine Learning, EHR, Gradient Boosted Trees, Predictive Modeling, SHAP Analysis, Dementia Risk, Early DiagnosisAbstract
Alzheimer’s Disease and Related Dementias (ADRD) are progressive neurodegenerative disorders posing significant social, clinical, and economic burdens. This study aims to enhance early diagnosis of ADRD using advanced machine learning (ML) models applied to longitudinal electronic health record (EHR) data from the University of Missouri Healthcare system. We evaluated six ML algorithms—Gradient Boosted Trees (GBT), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), and AdaBoost—over varying prediction windows from one to five years. The dataset included 123,735 patients aged 50 and above, with 4012 diagnosed cases of ADRD. A comprehensive set of demographics, clinical, and behavioural features were extracted and pre-processed to train the models. Among all models, GBT demonstrated the highest predictive accuracy, achieving an AUC of 0.833 in the five-year prediction window. SHAP (SHapley Additive exPlanations) analysis revealed key risk factors such as depression, age groups (70–80 and 80–90), sleep apnoea, and cardiovascular diseases. These results suggest that ML models, especially GBT, can provide high-performing, interpretable tools for early ADRD detection, informing clinical decision-making and enabling timely intervention strategies.
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