Estimation of Risk Factors Associated with Breast Cancer Worry with Using Artificial Neural Network Model

Year 2023, , 253 - 264, 01.09.2023

Gülçin Nacar , Feyza İnceoğlu , Sermin Timur

https://doi.org/10.38108/ouhcd.1185064

Abstract

Objective: In this study, it was aimed to determine the risk factors associated with breast cancer worry in women using binary logistic regression and artificial neural network (ANN) models.
Method: The study was conducted with 365 women aged 18 and over, reached in a Family Health Center. Accuracy rate and the area under the ROC curve were used to determine the performance of the multilayer perceptron neural network model used to identify the factors associated with breast cancer worry. Research data were collected using a personal information form and Breast Cancer Worry Scale (BCWC).
Results: In the univariate sample tests, it was found that the BCWS scores showed statistically significant differences according to the variables of age, income, menopause and smoking (p<0.05). The correct classification rates for breast cancer worry of the created multilayer perceptual neural network model were calculated as 90.9% in the training dataset and 89% in the test dataset. Considering the importance values of the variables; educational status (98.9%) was found to be the most influential factor on breast cancer worry. In the binary logistic regression analysis, it was found that income status had a 2.384 fold effect on breast cancer worry (OR= 2.384, 95% CI 1.010-5.628). It is recommended that health professionals consider the identified risk factors when evaluating women's breast cancer worries.
Conclusion: In the Binary Logistic Regression model, the income status was found to be effective on the BCWC as in the univariate sample tests. Educational status, parity, and occupation variables, which are the highest risk factors in ANN analysis, were not found significant in univariate statistical tests. It has been determined that ANN analyses prevent data loss in parametric tests. It is recommended that health professionals consider the identified risk factors when evaluating women's breast cancer worries.

Keywords

Breast cancer worry, artificial neural network model, associated factors

Project Number

-

Yapay Sinir Ağı Modeli Kullanılarak Meme Kanseri Endişesi ile İlişkili Risk Faktörlerinin Tahmini

Abstract

Amaç: Bu çalışmada, kadınlarda meme kanseri endişesi ile ilişkili risk faktörlerinin binary lojistik regresyon ve yapay sinir ağı (YSA) modelleri kullanılarak belirlenmesi amaçlanmıştır.
Yöntem: Araştırma, bir aile sağlığı merkezinde, 18 yaş ve üzerinde olan 365 kadın ile 30 Nisan-15 Haziran 2021 tarihleri arasında yapılmıştır. Meme kanseri endişesi ile ilişkili faktörleri belirlemek için kullanılan çok katmanlı algılayıcı yapay sinir ağı modelinin performansını belirlemede, doğruluk oranı ve ROC eğrisinin altındaki alan kullanılmıştır. Araştırma verileri, Kişisel Bilgi Formu ve Meme Kanseri Endişe Skalası (MKES) kullanılarak toplanmıştır.
Bulgular: Tek değişkenli örneklem testlerinde MKES puanlarının yaş, gelir durumu, menopoz ve sigara içme değişkenlerine göre istatistiksel olarak anlamlı farklılık gösterdiği bulunmuştur (p<0.05). Oluşturulan çok katmanlı algısal sinir ağı modelinin meme kanseri endişesi için doğru sınıflandırma oranları eğitim veri setinde % 90.9 ve test veri setinde % 89 olarak hesaplanmıştır. Değişkenlerin önem değerleri dikkate alındığında; meme kanseri endişesi üzerinde en yüksek düzeyde etkili faktörün eğitim durumu (%98.9) olduğu bulunmuştur. Binary lojistik regresyon analizinde ise gelir durumunun meme kanseri endişesi üzerinde 2.384 kat etkili olduğu bulunmuştur (OR= 2.384, %95 CI 1.010-5.628).
Sonuç: Binary Lojistik Regresyon modelinde gelir durumu tek değişkenli örneklem testlerinde olduğu gibi MKES üzerinde etkili bulunmuştur. YSA analizinde en yüksek risk faktörü olan eğitim durumu, parite ve meslek değişkenleri tek değişkenli istatistiksel testlerde anlamlı bulunmamıştır. YSA analizlerinin parametrik testlerde var olan veri kayıplarını önlediği saptanmıştır. Sağlık profesyonellerinin kadınların meme kanseri endişesini değerlendirirken saptanan risk faktörlerini göz önünde bulundurmaları önerilmektedir.

Keywords

Meme kanseri endişesi, risk faktörleri, yapay sinir ağları

Supporting Institution

Bulunmamaktadır

Project Number

-

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