A novel ML approach to prediction of breast cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier

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dc.contributor.authorPolat, Kemal
dc.contributor.authorŞentürk, Ümit
dc.date.accessioned2021-06-23T19:50:01Z
dc.date.available2021-06-23T19:50:01Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) -- OCT 19-21, 2018 -- Kizilcahamam, TURKEYen_US
dc.description.abstractBreast cancer is the second most common cancer in our country and in the world. In this study, a breast cancer data set was formed from the findings obtained from experiments conducted in the city of Coimbra of Portugal. There are two sets of data (52 data: healthy group, 64 data belong to patient group) and 9 features in the breast cancer data set of 116 data, both healthy and patient. These nine features are: Age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, MCP-1. In the proposed method, a three-step hybrid structure is proposed to detect the presence of breast cancer. In the first step, the dataset was first normalized by the MAD normalization method. In the second step, k-means clustering (KMC) based feature weighting has been used for weighting the normalized data. Finally, the AdaBoostM1 classifier has been used to classify the weighted data set. Only the combination the AdaBoostM1 classifier with MAD normalization method yielded a 75% classification accuracy in the detection of breast cancer, whereas the hybrid approach achieved 91.37% success. These results show that the proposed system could be used safely to detect breast cancer.<bold> </bold>en_US
dc.description.sponsorshipIEEE Turkey Sect, Karabuk Univ, Kutahya Dumlupinar Univen_US
dc.identifier.endpage318en_US
dc.identifier.isbn978-1-5386-4184-2
dc.identifier.scopus2-s2.0-85060812412en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage315en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9679
dc.identifier.urihttps://ieeexplore.ieee.org/document/8567245
dc.identifier.urihttps://doi.org/10.1109/ISMSIT.2018.8567245
dc.identifier.wosWOS:000467794200056en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 2Nd International Symposium On Multidisciplinary Studies And Innovative Technologies (Ismsit)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast Canceren_US
dc.subjectMachine Learningen_US
dc.subjectDiagnosisen_US
dc.subjectFeature Weightingen_US
dc.subjectClassificationen_US
dc.subjectPrediction Algorithms
dc.subjectClassification Algorithms
dc.subjectMachine Learning
dc.subjectClustering Algorithms
dc.titleA novel ML approach to prediction of breast cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifieren_US
dc.typeConference Objecten_US

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