An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Ali Şenol; Mahmut Kaya

Makale detayları

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Yazarlar:

Ali Şenol | Mahmut Kaya

Yayıncı:

Bingol University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 1, pp.70-77

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314173

pISSN:

2149-6366

Anahtar kelimeler:

Özet:

 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 andor 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.

 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 andor 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.

 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 andor 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.

 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 andor 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.

Makale detayları

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Yazarlar:

Ali Şenol | Mahmut Kaya

Yayıncı:

Bingol University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 1, pp.70-77

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314173

pISSN:

2149-6366

Outlier detection

clustering

classification

breast cancer diagnosis

Sayı

Makale detayları

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Yazarlar: Ali Şenol | Mahmut Kaya

Yayıncı: Bingol University

Ana Başlıklar: Karar Desteği ve Grup Destek Sistemleri

Dergi: Türk Doğa ve Fen Dergisi Vol.13 Issue 1, pp.70-77

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001314173

pISSN: 2149-6366

Outlier detection

clustering

classification

breast cancer diagnosis

Sayı

Yazarlar:

Ali Şenol | Mahmut Kaya

Yayıncı:

Bingol University

Ana Başlıklar:

Karar Desteği ve Grup Destek Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.13 Issue 1, pp.70-77

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001314173

pISSN:

2149-6366