An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis
| dc.contributor.author | Şenol, Ali | |
| dc.contributor.author | Kaya, Mahmut | |
| dc.date.accessioned | 2025-03-17T12:18:35Z | |
| dc.date.available | 2025-03-17T12:18:35Z | |
| dc.date.issued | 2024 | |
| dc.department | Tarsus Üniversitesi | |
| dc.description.abstract | Classification algorithms are commonly used as a decision support system for diagnosing many diseases, such as breast cancer. The accuracy of classification algorithms can be affected negatively if the data contains outliers and/or noisy data. For this reason, outlier detection methods are frequently used in this field. In this study, we propose and compare various models that use clustering algorithms to detect outliers in the data preprocessing stage of classification to investigate their effects on classification accuracy. Clustering algorithms such as DBSCAN, HDBSCAN, OPTICS, FuzzyCMeans, and MCMSTClustering (MCMST) were used separately in the data preprocessing stage of the k Nearest Neighbor (kNN) classification algorithm for outlier elimination, and then the results were compared. According to the obtained results, MCMST algorithm was more successful in outlier elimination. The classification accuracy of the kNN + MCMST model was 0.9834, which was the best one, while the accuracy of kNN algorithm without using any data preprocessing was 0.9719. | |
| dc.identifier.doi | 10.46810/tdfd.1364397 | |
| dc.identifier.endpage | 77 | |
| dc.identifier.issn | 2149-6366 | |
| dc.identifier.issue | 1 | |
| dc.identifier.startpage | 70 | |
| dc.identifier.trdizinid | 1230544 | |
| dc.identifier.uri | https://doi.org/10.46810/tdfd.1364397 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1230544 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.13099/881 | |
| dc.identifier.volume | 13 | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.language.iso | en | |
| dc.relation.ispartof | Türk Doğa ve Fen Dergisi | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_TR_20250316 | |
| dc.subject | Classification | |
| dc.subject | clustering | |
| dc.subject | Outlier detection | |
| dc.subject | breast cancer diagnosis | |
| dc.title | An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis | |
| dc.type | Article |
