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There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded \u223c72% of area under the ROC curve.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3158-x","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T14:02:48Z","timestamp":1576504968000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Benchmarking machine learning models for late-onset alzheimer\u2019s disease prediction from genomic data"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7556-245X","authenticated-orcid":false,"given":"Javier","family":"De Velasco Oriol","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edgar E.","family":"Vallejo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karol","family":"Estrada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Gerardo","family":"Tam\u00e9z Pe\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"The Alzheimer\u2019s","family":"Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"issue":"8","key":"3158_CR1","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.arcmed.2012.11.003","volume":"43","author":"Ana Luisa Sosa-Ortiz","year":"2012","unstructured":"Sosa-Ortiz AL, Acosta-Castillo I, Prince MJ. 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