A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data

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dc.authoridTutsoy, Onder/0000-0001-6385-3025
dc.contributor.authorTutsoy, Önder
dc.contributor.authorSumbul, Hilmi Erdem
dc.date.accessioned2025-01-06T17:36:39Z
dc.date.available2025-01-06T17:36:39Z
dc.date.issued2024
dc.description.abstractThyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.
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.doi10.1093/bib/bbae344
dc.identifier.issn1467-5463
dc.identifier.issn1477-4054
dc.identifier.issue4
dc.identifier.pmid39007597
dc.identifier.scopus2-s2.0-85199016207
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1093/bib/bbae344
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1936
dc.identifier.volume25
dc.identifier.wosWOS:001283817400002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofBriefings in Bioinformatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectbig data
dc.subjectdeep learning
dc.subjectdimension reduction
dc.subjectfeature selection
dc.subjectmissing data
dc.subjectsize reduction
dc.subjectthyroid cancer
dc.titleA novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data
dc.typeArticle

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