Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification

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dc.contributor.authorTutsoy, Önder
dc.contributor.authorKoc, Gizem Gul
dc.date.accessioned2025-01-06T17:36:42Z
dc.date.available2025-01-06T17:36:42Z
dc.date.issued2024
dc.description.abstractBackgroundBlood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.ResultsThis paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.ConclusionsThe results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
dc.description.sponsorshipTurkish Scientific and Research Councel of Turkey
dc.description.sponsorshipThis work was supported by the Turkish Scientific and Research Councel of Turkey.
dc.identifier.doi10.1186/s12859-024-05729-2
dc.identifier.issn1471-2105
dc.identifier.issue1
dc.identifier.pmid38459463
dc.identifier.scopus2-s2.0-85186766532
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1186/s12859-024-05729-2
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1968
dc.identifier.volume25
dc.identifier.wosWOS:001181103100002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherBmc
dc.relation.ispartofBmc Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectBlood test
dc.subjectDeep machine learning
dc.subjectFeature elimination
dc.subjectFeature selection
dc.subjectHealth risks classification
dc.subjectSelf-feature weighting
dc.subjectSelf-supervised learning
dc.titleDeep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification
dc.typeArticle

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