Predicting cognitive decline in Alzheimer’s disease using minimal clinical features: A machine learning approach with the NACC cohort - Tarkesh Esfahani, Maryam (Journal of Composites and Compounds, 2024)
Early identification of individuals at risk for Alzheimer’s disease–related cognitive decline is crucial for timely intervention and clinical trial enrollment. We developed a machine learning model using only five routinely collected clinical variables, age, sex, education, baseline Mini-Mental State Examination (MMSE), and Clinical Dementia Rating–Sum of Boxes (CDR-SB), to predict cognitive decline three years in advance. Using a sample of 2,000 participants from the National Alzheimer’s Coordinating Center (NACC) dataset, a Random Forest classifier achieved 94% accuracy and an AUC of 0.98 on an independent test set. Feature importance analysis confirmed that CDR-SB and MMSE were the strongest predictors, collectively accounting for 66% of model relevance. This approach offers a low-cost, scalable tool for risk stratification, particularly valuable in low-resource settings and primary care, where advanced diagnostics are unavailable.