{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T12:03:21Z","timestamp":1780920201733,"version":"3.54.1"},"reference-count":37,"publisher":"Wiley","issue":"S1","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81973144"],"award-info":[{"award-number":["81973144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Geriatrics Gerontology Int"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec><jats:title>Aim<\/jats:title><jats:p>Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI\u2010related markers could predict the onset of MCI early.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3\u2009years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI\u2010related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The baseline age of the study sample was 74.8\u2009years old. The best\u2010performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>With the help of machine learning, fewer neuropsychological tests and MRI\u2010related markers are required to accurately predict MCI within 3\u2009years, thereby facilitating targeted intervention. <jats:bold>Geriatr Gerontol Int 2024; 24: 96\u2013101<\/jats:bold>.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ggi.14670","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T01:43:16Z","timestamp":1695346996000},"page":"96-101","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Predicting mild cognitive impairment in older adults: A machine learning analysis of the <scp>Alzheimer's Disease Neuroimaging Initiative<\/scp>"],"prefix":"10.1111","volume":"24","author":[{"given":"Yafei","family":"Wu","sequence":"first","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-3234","authenticated-orcid":false,"given":"Ya","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"},{"name":"National Institute for Data Science in Health and Medicine Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcp.2013.12.024"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211558"},{"key":"e_1_2_9_4_1","unstructured":"MarinescuRV OxtobyNP YoungALet al.Tadpole challenge: prediction of longitudinal evolution in Alzheimer's disease 2018."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/msj.20279"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(06)68542-5"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h3029"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.055148"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/alz.052545"},{"key":"e_1_2_9_10_1","first-page":"191","article-title":"Diagnostic and statistical manual of mental disorders (DSM)","volume":"25","author":"Battle DE","year":"2013","journal-title":"Codas"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2020.04.026"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-018-1932-x"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dadm.2018.07.004"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Samper\u2010Gonz\u00e1lezJ BurgosN FontanellaSet al.Yet another ADNI machine learning paper? 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