Big data dimensionality reduction-based supervised machine learning algorithms for NASH diagnosis

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dc.contributor.authorTutsoy, Onder
dc.contributor.authorOzturk, Huseyin Ali
dc.contributor.authorSumbul, Hilmi Erdem
dc.date.accessioned2026-02-27T07:33:13Z
dc.date.available2026-02-27T07:33:13Z
dc.date.issued2025
dc.description.abstractBackgroundIdentifying the Non-Alcoholic Steatohepatitis (NASH) that can cause liver failure-based morbidity remains a challenging research problem since there is no confirmed and effective approach for its early and accurate diagnosis yet. A large amount of medical data is collected to diagnose the NASH where the majority of them are redundant.MethodsThis paper initially focuses on selecting the most informative blood test data among the collected big data with the Pearson correlation statistical approach and modified Particle Swarm Optimization with Artificial Neural Networks (PSO-ANN) machine learning algorithm. Then, a gradient based Batch Least Squares (BLS) and a search-based Artificial Bee Colony (ABC) machine learning algorithms are implemented to optimize the NASH prediction models. Confirmed operational NASH diagnosis supervise the statistical and machine learning algorithms to develop accurate prediction models.ResultsTwo machine learning algorithms were trained and also validated with the varying number of selected input features. The results yielded that the trained BLS machine learning model is able to diagnose benign and malignant cases with 100% and 98% accuracies, respectively. The trained ABC machine learning algorithm diagnoses the benign and malignant cases with 90.5% and 94.3% accuracies, respectively.
dc.identifier.doi10.1186/s12859-025-06263-5
dc.identifier.issn1471-2105
dc.identifier.issue1
dc.identifier.pmid41120851
dc.identifier.urihttp://dx.doi.org/10.1186/s12859-025-06263-5
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4497
dc.identifier.volume26
dc.identifier.wosWOS:001597396100001
dc.indekslendigikaynakWeb of Science
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_20260302
dc.subjectBig data
dc.subjectDimension reduction
dc.subjectFeature selection
dc.subjectPrediction model
dc.subjectNASH disease
dc.subjectSupervised machine learning
dc.titleBig data dimensionality reduction-based supervised machine learning algorithms for NASH diagnosis
dc.typeArticle

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