A hybrid model for classification of medical data set based on factor analysis and extreme learning machine: FA + ELM

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dc.contributor.authorKaya, Yılmaz
dc.contributor.authorKuncan, Fatma
dc.date.accessioned2024-12-24T19:10:07Z
dc.date.available2024-12-24T19:10:07Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractData mining techniques such as classification, clustering, and prediction are used extensively for medical diagnosis in epidemiological fields. A hybrid model based on Factor Analysis (FA) and Extreme Learning Machine (ELM) was proposed in this study for diagnosing breast cancer, Lymphography, and erythemato-squamous diseases. The proposed hybrid model consists of two stages. Firstly, FA was used for preprocessing the medical dataset, and then, the factors obtained using FA were used as input features for the ELM model. Dermatology, Lymphography, and Wisconsin Breast Cancer real datasets obtained from the UCI machine learning database were used to test the proposed model. An average success rate of 96.39 % and 96.94 % was obtained after classifying the dermatology dataset with ELM and FA + ELM models. While the success rate obtained by classifying the lymphography data set using ELM is 84.50 %, the result obtained with FA + ELM is 85.10 %. The success rates of 97.10 % and 97.25 % are achieved respectively for Wisconsin Breast Cancer (WBC) using ELM and FA + ELM. As a result, it was observed that preprocessing of the data increased the average classification success in three different medical datasets used for the classification problem. It is considered that the proposed hybrid model will be helpful for the decision-making stage in medical diagnosis systems. © 2022 Elsevier Ltd
dc.description.sponsorshipSiirt University Faculty of Engineering Machine Vision
dc.identifier.doi10.1016/j.bspc.2022.104023
dc.identifier.issn1746-8094
dc.identifier.scopus2-s2.0-85135334174
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org10.1016/j.bspc.2022.104023
dc.identifier.urihttps://hdl.handle.net/20.500.12604/3933
dc.identifier.volume78
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectBreast cancer
dc.subjectDermatological diseases
dc.subjectExpert system
dc.subjectExtreme learning machine
dc.subjectFactor analysis
dc.subjectLymphography
dc.subjectMachine learning
dc.subjectMedical datasets
dc.titleA hybrid model for classification of medical data set based on factor analysis and extreme learning machine: FA + ELM
dc.typeArticle

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