Trial Ready Cohort for Preclinical/Prodromal Alzheimer's Dementia (TRCPAD) aims to develop a large, biomarker-confirmed, trial-ready cohort to facilitate rapid enrollment into AD prevention trials. Preclinical Alzheimer’s studies to date have had more than a 2/3rd amyloid screen fail rate, resulting in prolonged and expensive recruitment. One of our aims is to optimize an innovative, adaptive risk algorithm to efficiently identify the most appropriate trial participants. Participants are enrolling in our web-based registry https://www.aptwebstudy.org/ where they complete a number of questionnaires and cognitive assessments. With these data, we assess their eligibility for in-clinic assessments via a multi-stage algorithm and use Machine Learning techniques to predict amyloid status. Participants who are confirmed amyloid positive are invited into clinical trials of anti-amyloid interventions. In this presentation, we will take a detailed look at the Machine Learning methods chosen and our motivations for these choices. We will also see how the addition of APOE genotyping significantly improves the predictive performance, AUC (~0.6 to ~0.7), when estimating Amyloid status.