Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

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dc.authorscopusid57197276230
dc.authorscopusid57669060800
dc.authorscopusid57200277535
dc.contributor.authorOzcan, Irem
dc.contributor.authorAydin, Hakan
dc.contributor.authorCetinkaya, Ali
dc.date.accessioned2024-09-11T19:57:55Z
dc.date.available2024-09-11T19:57:55Z
dc.date.issued2022
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractObjective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Result: Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%.Conclusion: When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types. © This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.en_US
dc.identifier.doi10.31557/APJCP.2022.23.10.3287
dc.identifier.endpage3297en_US
dc.identifier.issn1513-7368en_US
dc.identifier.issue10en_US
dc.identifier.pmid36308351en_US
dc.identifier.scopus2-s2.0-85141004855en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3287en_US
dc.identifier.urihttps://doi.org/10.31557/APJCP.2022.23.10.3287
dc.identifier.urihttps://hdl.handle.net/11363/8357
dc.identifier.volume23en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAsian Pacific Organization for Cancer Preventionen_US
dc.relation.ispartofAsian Pacific Journal of Cancer Preventionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectBreast cancer; Classification; Data management; Information systems; Machine learningen_US
dc.titleComparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Canceren_US
dc.typeArticleen_US

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