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Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\beta$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03b2<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\beta$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03b2<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.<\/jats:p>","DOI":"10.1038\/s41598-021-83911-9","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T11:07:32Z","timestamp":1614596852000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Machine learning methods to predict amyloid positivity using domain scores from cognitive tests"],"prefix":"10.1038","volume":"11","author":[{"given":"Guogen","family":"Shan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Bernick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jessica Z. 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