The Effects of Variable Selection and Dimension Reduction Methods on the Classification Model in the Small Round Blue Cell Tumor Dataset

Fatma Hilal YAĞIN; Zeynep KÜÇÜKAKÇALI; İpek BALIKÇI ÇİÇEK; Harika GÖZÜKARA BAĞ

doi:https://doi.org/10.19127/mbsjohs.994625

Makale detayları

The Effects of Variable Selection and Dimension Reduction Methods on the Classification Model in the Small Round Blue Cell Tumor Dataset

Yazarlar:

Fatma Hilal YAĞIN | Zeynep KÜÇÜKAKÇALI | İpek BALIKÇI ÇİÇEK | Harika GÖZÜKARA BAĞ

Yayıncı:

Ordu University

Ana Başlıklar:

Sağlık teşvikleri

Dergi:

Middle Black Sea Journal of Health Science 2021 Vol.7 Issue 3, pp.390-396

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000563890

Sıklık:

Üç Yıllık

eISSN:

2149-7796

DOI:

https://doi.org/10.19127/mbsjohs.994625

Özet:

 

Objective: The purpose of this study is to investigate and compare the effects of different dimension reduction methods (PCA, ICA, PCA + Forward Selection, ICA + Forward Selection) on the K-NN classifier using open access gene expression data of small round blue cell tumor types.Methods: In this study, open access gene expression data of small round blue cell tumor types was used for investigate and compare the effects of different dimension reduction methods. In the study, PCA, ICA, PCA + Forward Selection, ICA + Forward Selection were used as different dimension reduction methods together with K-NN classification method.Results: Accuracy values obtained from the dimension reduction model made with PCA on K-NN model; for EWS, BL, NB, and RMS type tumors with 93.51%, 91.14%, 92.31%, and 94.74% respectively. Accuracy values obtained from the dimension reduction model made with PCA + Forward Selection on K-NN model; for EWS, BL, NB, and RMS type tumors with 96.25%, 96.25%, 95.06% and 95.47%, respectively. Accuracy values obtained from the dimension reduction model made with ICA on K-NN model; for EWS, BL, NB, and RMS type tumors with 91.89%, 90.67%, 88.31% and 89.47% respectively. Accuracy values obtained from the dimension reduction model made with ICA+ Forward Selection on K-NN model; for EWS, BL, NB, and RMS type tumors with 93.51%, 91.14%, 92.31% and 94.74% respectively.Conclusion: In this study, the model created with PCA gives higher results than the model created with ICA. In addition, according to the results of the models obtained by applying the Forward selection method on these 2 models, the forward selection method has increased the classification performance.

Makale detayları

The Effects of Variable Selection and Dimension Reduction Methods on the Classification Model in the Small Round Blue Cell Tumor Dataset

Yazarlar: Fatma Hilal YAĞIN | Zeynep KÜÇÜKAKÇALI | İpek BALIKÇI ÇİÇEK | Harika GÖZÜKARA BAĞ

Yayıncı: Ordu University

Ana Başlıklar: Sağlık teşvikleri

Dergi: Middle Black Sea Journal of Health Science 2021 Vol.7 Issue 3, pp.390-396

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000563890

Sıklık: Üç Yıllık

eISSN: 2149-7796

DOI: https://doi.org/10.19127/mbsjohs.994625

Sayı

Yazarlar:

Fatma Hilal YAĞIN | Zeynep KÜÇÜKAKÇALI | İpek BALIKÇI ÇİÇEK | Harika GÖZÜKARA BAĞ

Yayıncı:

Ordu University

Ana Başlıklar:

Sağlık teşvikleri

Dergi:

Middle Black Sea Journal of Health Science 2021 Vol.7 Issue 3, pp.390-396

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000563890

Sıklık:

Üç Yıllık

eISSN:

2149-7796

DOI:

https://doi.org/10.19127/mbsjohs.994625