Prediction of Dementia Subtypes by Machine Learning: Feasibility of Error Control

Accurate prediction of dementia subtypes is important in geriatric decision‐making and in designing clinical trials. We used machine learning and a novel error control algorithm to analyze longitudinal data collected by the National Alzheimer Coordinating Center (NACC) from June 2005 to November 2021. Our goals were (1) Predicting the conversion to several dementia subtypes in patients who were dementia‐free at baseline; (2) Finding important clinical metrics and risk factors that contribute to the prediction; (3) Controlling the prediction error rate on specific dementia subtypes to help design clinical trials.