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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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Bmc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
BackgroundBlood 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.
Açıklama
Anahtar Kelimeler
Blood test, Deep machine learning, Feature elimination, Feature selection, Health risks classification, Self-feature weighting, Self-supervised learning
Kaynak
Bmc Bioinformatics
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
25
Sayı
1
