Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study
Yıl: 2021 Cilt: 13 Sayı: 3 Sayfa Aralığı: 266 - 272 Metin Dili: İngilizce DOI: 10.5336/biostatic.2021-84349 İndeks Tarihi: 14-05-2022
Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study
Öz: Objective: Associative classification is a method thatgenerates a rule-based classifier in a categorical data set. The mainpurpose of the associative classification is to create classificationmodels with high performance and, in addition, to improve inter pretability thanks to the rules it creates. In this study, it is aimed toclassify, predict cervical cancer with the methods of relational clas sification and to determine the most important parameters and rela tional rules associated with the disease. Material and Methods: Inthe study, regular class association rules (RCAR) and classificationbased on associations (CBA) methods were applied to the openaccess data set named “Cervical Cancer Behavioral Risk Data Set”and the results were compared. In order to separate the numericalvariables in the data set, Boruta feature selection method was ap plied to determine the most important features about Ameva andcervical cancer. The performances of the created relational classifi cation models were evaluated with accuracy, balanced accuracy,sensitivity, specificity, Matthews correlation coefficient (MCC), G mean, diagnostic accuracy, Youden’s index, positive predictivevalue, negative predictive value and F1-score criteria. Results: Ac cording to CBA model results, sensitivity is 100%, specificity 98%,accuracy 98.6%, balanced accuracy 99%, Youden's index 98%,MCC 96.7%, diagnostic accuracy 98.6%, G-mean 97.7%, negativepredictive value 1%, positive predictive value 95.5%, and F1 score97.7%. According to RCAR model results, sensitivity is 90.5%,specificity 98%, accuracy 95.8%, balanced accuracy 94.3%,Youden's index 88.5%, MCC 89.8%, diagnostic accuracy 95.8%,G-mean 95.6%, negative predictive value 96.2%, positive predic tive value 95%, and F1 score 92.7%. Conclusion: When the resultsare examined, it can be said that the CBA model is more successfulin classifying cervical cancer compared to the RCAR model. Inaddition, the relational classification models created in this studyand the rules obtained regarding the disease are promising in termsof their use in early diagnosis and preventive medicine practices forcervical cancer.
Anahtar Kelime: Rahim Ağzı Kanseri Tahminî İçin İlişkisel SınıflandırmaYöntemlerinin Performanslarının Karşılaştırılması: Gözlemsel Çalışma
Öz: Amaç: İlişkili sınıflandırma, kategorik bir veri kümesindekural tabanlı bir sınıflandırıcı oluşturan bir yöntemdir. İlişkisel sı nıflandırmanın temel amacı, yüksek performanslı sınıflandırmamodelleri oluşturmak ve ayrıca oluşturduğu kurallar sayesinde yo rumlanabilirliği artırmaktır. Bu çalışmada, rahim ağzı kanserininilişkisel sınıflandırma yöntemleri ile serviks kanserinin sınıflandı rılması, tahmin edilmesi ve hastalıkla ilişkili en önemli parametre lerin ve ilişkisel kuralların belirlenmesi amaçlanmıştır. Gereç veYöntemler: Çalışmada “Rahim Ağzı Kanseri Davranışsal RiskVeri Seti” adlı açık erişim veri setine düzenli sınıf ilişkilendirmekuralları [regular class association rules (RCAR)] ve ilişki kuralla rına dayalı sınıflandırma [classification based on association(CBA)] yöntemleri uygulanmış ve sonuçlar karşılaştırılmıştır. Verisetindeki sayısal değişkenleri ayırmak amacıyla Ameva ve rahimağzı kanseri ile ilgili en önemli özellikleri belirlemek için Borutaözellik seçme yöntemi uygulanmıştır. Oluşturulan ilişkisel sınıflan dırma modellerinin performansları; doğruluk, dengeli doğruluk,duyarlılık, özgüllük, Matthews korelasyon katsayısı [Matthewscorrelation coefficient (MCC)], G-ortalama, tanısal doğruluk,Youden indeksi, pozitif tahmin değeri, negatif tahmin değeri ve F1skor kriterleri ile değerlendirildi. Bulgular: CBA model sonuçlarınagöre duyarlılık %100, özgüllük %98, doğruluk %98,6, dengeli doğru luk %99, Youden indeksi %98, MCC %96,7, tanısal doğruluk %98,6,G-ortalama %97,7, negatif tahmin değeri %1, pozitif tahmin değeri%95,5 ve F1 puanı %97,7’dir. RCAR model sonuçlarına göre duyar lılık %90,5, özgüllük %98, doğruluk %95,8, dengeli doğruluk %94,3,Youden indeksi %88,5, MCC %89,8, tanısal doğruluk %95,8, G ortalama %95,6, negatif tahmin değeri %96,2, pozitif tahmin değeri%95 ve F1 puanı %92,7. Sonuç: Sonuçlar incelendiğinde, CBA mo delinin RCAR modeline göre rahim ağzı kanserini sınıflandırmadadaha başarılı olduğu söylenebilir. Ayrıca bu çalışmada oluşturulanilişkisel sınıflandırma modelleri ve hastalığa ilişkin elde edilen kural lar, rahim ağzı kanserine yönelik erken tanı ve koruyucu hekimlikuygulamalarında kullanılması açısından umut vericidir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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| APA | YAĞIN F, Yağın B, ARSLAN A, ÇOLAK C (2021). Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. , 266 - 272. 10.5336/biostatic.2021-84349 |
| Chicago | YAĞIN Fatma Hilal,Yağın Burak,ARSLAN Ahmet Kadir,ÇOLAK Cemil Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. (2021): 266 - 272. 10.5336/biostatic.2021-84349 |
| MLA | YAĞIN Fatma Hilal,Yağın Burak,ARSLAN Ahmet Kadir,ÇOLAK Cemil Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. , 2021, ss.266 - 272. 10.5336/biostatic.2021-84349 |
| AMA | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. . 2021; 266 - 272. 10.5336/biostatic.2021-84349 |
| Vancouver | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. . 2021; 266 - 272. 10.5336/biostatic.2021-84349 |
| IEEE | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C "Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study." , ss.266 - 272, 2021. 10.5336/biostatic.2021-84349 |
| ISNAD | YAĞIN, Fatma Hilal vd. "Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study". (2021), 266-272. https://doi.org/10.5336/biostatic.2021-84349 |
| APA | YAĞIN F, Yağın B, ARSLAN A, ÇOLAK C (2021). Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. Türkiye Klinikleri Biyoistatistik Dergisi, 13(3), 266 - 272. 10.5336/biostatic.2021-84349 |
| Chicago | YAĞIN Fatma Hilal,Yağın Burak,ARSLAN Ahmet Kadir,ÇOLAK Cemil Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. Türkiye Klinikleri Biyoistatistik Dergisi 13, no.3 (2021): 266 - 272. 10.5336/biostatic.2021-84349 |
| MLA | YAĞIN Fatma Hilal,Yağın Burak,ARSLAN Ahmet Kadir,ÇOLAK Cemil Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. Türkiye Klinikleri Biyoistatistik Dergisi, vol.13, no.3, 2021, ss.266 - 272. 10.5336/biostatic.2021-84349 |
| AMA | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(3): 266 - 272. 10.5336/biostatic.2021-84349 |
| Vancouver | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(3): 266 - 272. 10.5336/biostatic.2021-84349 |
| IEEE | YAĞIN F,Yağın B,ARSLAN A,ÇOLAK C "Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study." Türkiye Klinikleri Biyoistatistik Dergisi, 13, ss.266 - 272, 2021. 10.5336/biostatic.2021-84349 |
| ISNAD | YAĞIN, Fatma Hilal vd. "Comparison of Performances of Associative ClassificationMethods for Cervical Cancer Prediction: Observational Study". Türkiye Klinikleri Biyoistatistik Dergisi 13/3 (2021), 266-272. https://doi.org/10.5336/biostatic.2021-84349 |
