{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:46:48Z","timestamp":1778086008516,"version":"3.51.4"},"reference-count":105,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer\u2019s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.<\/jats:p>","DOI":"10.3390\/diagnostics11050887","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T04:25:07Z","timestamp":1621225507000},"page":"887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3146-7899","authenticated-orcid":false,"given":"Jorge I.","family":"V\u00e9lez","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0975-674X","authenticated-orcid":false,"given":"Luiggi A.","family":"Samper","sequence":"additional","affiliation":[{"name":"Department of Public Health, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauricio","family":"Arcos-Holzinger","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Psiquiatr\u00eda (GIPSI), Departamento de Psiquiatr\u00eda, Instituto de Investigaciones M\u00e9dicas, Facultad de Medicina, Universidad de Antioquia, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lady G.","family":"Espinosa","sequence":"additional","affiliation":[{"name":"INPAC Research Group, Fundaci\u00f3n Universitaria Sanitas, Bogot\u00e1 111321, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario A.","family":"Isaza-Ruget","sequence":"additional","affiliation":[{"name":"INPAC Research Group, Fundaci\u00f3n Universitaria Sanitas, Bogot\u00e1 111321, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Lopera","sequence":"additional","affiliation":[{"name":"Neuroscience Research Group, University of Antioquia, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauricio","family":"Arcos-Burgos","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Psiquiatr\u00eda (GIPSI), Departamento de Psiquiatr\u00eda, Instituto de Investigaciones M\u00e9dicas, Facultad de Medicina, Universidad de Antioquia, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.phrs.2018.04.017","article-title":"Stress as risk factor for Alzheimer\u2019s disease","volume":"132","author":"Caruso","year":"2018","journal-title":"Pharmacol. 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