Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data

Basit Öğe Kaydı
dc.authoridKalinli, Adem/0000-0001-5848-7876
dc.authoridSARICA, Burak/0000-0001-5926-9440
dc.authoridHaznedar, Bulent/0000-0003-0692-9921
dc.contributor.authorHaznedar, Bulent
dc.contributor.authorArslan, Mustafa Turan
dc.contributor.authorKalinli, Adem
dc.date.accessioned2024-09-18T20:25:16Z
dc.date.available2024-09-18T20:25:16Z
dc.date.issued2021
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIn the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using statistical algorithms is often limited. Recently, there has been an important increase in the studies on the utilization of artificial intelligence methods, for the purpose of classifying large-scale data. In this context, a hybrid approach based on the adaptive neuro-fuzzy inference system (ANFIS), the fuzzy c-means clustering (FCM), and the simulated annealing (SA) algorithm is proposed in this study. The proposed method is applied to classify five different cancer datasets (i.e., lung cancer, central nervous system cancer, brain cancer, endometrial cancer, and prostate cancer). The backpropagation algorithm, hybrid algorithm, genetic algorithm, and the other statistical methods such as Bayesian network, support vector machine, and J48 decision tree are used to compare the proposed approach's performance to other algorithms. The results show that the performance of training FCM-based ANFIS using SA algorithm for classifying all the cancer datasets becomes more successful with the average accuracy rate of 96.28% and the results of the other methods are also satisfactory. The proposed method gives more effective results than the others for classifying DNA microarray cancer gene expression data.en_US
dc.identifier.doi10.1007/s11517-021-02331-z
dc.identifier.endpage509en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue3en_US
dc.identifier.pmid33543413en_US
dc.identifier.scopus2-s2.0-85100508676en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage497en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-021-02331-z
dc.identifier.urihttps://hdl.handle.net/20.500.12483/10202
dc.identifier.volume59en_US
dc.identifier.wosWOS:000614781300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy neural networksen_US
dc.subjectSimulated annealingen_US
dc.subjectMachine learningen_US
dc.subjectOptimizationen_US
dc.subjectGene expressionen_US
dc.titleOptimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer dataen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ N/A ]
İsim:
10202.pdf
Boyut:
793.33 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu