Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
The Prediction of Breast Biopsy Outcomes Using Two Data Mining Algorithms Based on Parameter Variations
Meme Biyopsisi Sonuçlarının İki Veri Madenciliği Algoritması Kullanılarak Parametre Varyasyonlarına Dayalı Tahmin Edilmesi
Batuhan BAKIRARARa, İrem KARa, Derya GÖKMENa, Atilla Halil ELHANa, Volkan GENÇb
Departments of
aBiostatistics,
bSurgery, Ankara University Faculty of Medicine, Ankara, TURKEY
Turkiye Klinikleri J Biostat. 2019;11(2):133-42
doi: 10.5336/biostatic.2019-64754
Article Language: EN
Full Text
ABSTRACT
 Objective: Breast cancer is the type of cancer that causes the most death in women in the United States after lung cancer. The objective of this study was to predict breast biopsy results using age, BI-RADS, Mass Shape, Mass Margin, Mass Density by Multilayer Perceptron and Random Forest algorithms. Material and Methods: The dataset contains a BI-RADS assessment, the patient's age and three BI-RADS attributes together with the ground truth for 516 benign and 445 malignant masses. WEKA software was used for data mining. The data mining methods of the Multilayer Perceptron and Random Forest were used to predict the severity of cancer. Results: Accuracy, F-measure and Root Mean Squared Error values of the Multilayer Perceptron model were found 0.837, 0.833 and 0.352, respectively while accuracy, F-measure and Root Mean Squared Error values of the Random Forest model were found 0.816, 0.814 and 0.396, respectively. The Multilayer Perceptron method provided a better prediction of breast cancer diagnosis than the Random Forest method and a software was developed based on the best model created by the Multilayer Perceptron method. Conclusion: Consequently, a model that was built with the MLP method can be used as an alternative in the diagnosis of patients and be an assistant tool for physicians.

Keywords: Breast cancer; data mining; multilayer perceptron; prediction; random forest
ÖZET
 Amaç: Meme kanseri, ABD'de akciğer kanserinden sonra kadınlarda en fazla ölüme neden olan kanser türüdür. Bu çalışmanın amacı yaş, BI-RADS, Kitle Şekli, Kitle Marjı ve Kitle Yoğunluğu değişkenleri yardımıyla Çok Katmanlı Algılayıcı ve Random Forest algoritmaları kullanılarak meme biyopsi sonuçlarını tahmin etmektir. Gereç ve Yöntemler: Veri seti 516 iyi huylu ve 445 kötü huylu kitleye yönelik kesin referansla beraber, BI-RADS değerlendirmesi, hastanın yaşı ve üç BI-RADS özelliği değişkenlerinden oluşmaktadır. Veri madenciliği için WEKA yazılımı kullanılmıştır. MLP ve Random Forest veri madenciliği yöntemleri kanser sonuçlarını tahmin etmek için kullanılmıştır. Bulgular: Çok Katmanlı Algılayıcı modelinin doğruluk, F-ölçütü ve ortalama hata kareleri kare kökü değerleri sırasıyla 0.837, 0.833 ve 0.352 bulunurken, Random Forest modelinin doğruluk, F-ölçütü ve ortalama hata kareleri kare kökü değerleri sırasıyla 0.816, 0.814 ve 0.396 olarak bulunmuştur. Çok Katmanlı Algılayıcı yöntemi, meme kanseri teşhisi için Random Forest yönteminden daha iyi bir tahmin sağlamıştır, bu yüzden Çok Katmanlı Algılayıcı yöntemi kullanılarak oluşturulan en iyi modele dayalı bir yazılım geliştirilmiştir. Sonuç: Sonuç olarak, Çok Katmanlı Algılayıcı yöntemiyle yapılan bir model hastaların teşhisinde alternatif olarak kullanılabilir ve hekimler için yardımcı bir araç olabilir.

Anahtar Kelimeler: Meme kanseri; veri madenciliği; çok katmanlı algılayıcı; tahmin; random forest
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