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Two ML methods were used to develop the prediction models. Both knowledge\u2010driven and data\u2010driven approaches were explored. Four computable phenotyping algorithms were tested.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The gradient boosting tree (GBT) models trained with the data\u2010driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. 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