ArticleRevista de neurologia2026
AI-Enabled Modeling for Alzheimer's Disease Risk Prediction and Validation.
Article in Revista de neurologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTo investigate the multimodal clinical influencing factors of Alzheimer's disease (AD) onset, and to establish and test a risk prediction tool derived from these determinants and additional clinical measures, thus facilitating early intervention and risk classification in individuals at high risk for AD.
methodsA retrospective cohort of 502 high-risk individuals for AD (exhibiting cognitive decline or family history) who visited our hospital was included. A total of 502 participants were randomly split into a training cohort (n = 350) and a validation cohort (n = 152) in a 7:3 proportion. Demographic characteristics, clinical indicators, biomarkers, and genetic markers were collected. In the training set, univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were first applied for variable screening, followed by multivariate logistic regression to pinpoint independent influencing factors. Random forest (RF), XGBoost, and deep learning models were constructed using Python, with performance evaluated using area under the curve (AUC). The optimal model was selected, and feature importance was analyzed.
resultsBetween the training and validation sets, no statistically significant baseline characteristic differences were found (
conclusionThe RF model, based on integrated multimodal clinical influencing factors and clinical indicators, demonstrates potential for AD risk stratification in high-risk populations when evaluated on a validation cohort.
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