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

Year 2023, Volume: 6 Issue: 2, 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

-

References

• Anderson KN, Schwab RB, Martinez ME. (2014). Reproductive risk factors and breast cancer subtypes: a review of the literature. Breast Cancer Research and Treatment, 144(1), 1–10. https://doi.org/10.1007/S10549-014-2852-7
• Aoki RLF, Uong SP, Gomez SL, Alexeeff SE, Caan BJ, Kushi LH, et al. (2021). Individual- and neighborhood-level socioeconomic status and risk of aggressive breast cancer subtypes in a pooled cohort of women from Kaiser Permanente Northern California. Cancer, 127(24), 4602-4612. https://doi.org/10.1002/CNCR.33861
• Bennett P, Parsons E, Brain K, Hood K. (2010). Long-term cohort study of women at intermediate risk of familial breast cancer: experiences of living at risk. Psycho-Oncology, 19(4), 390–398. https://doi.org/ 10.1002/PON.1588
• Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394–424. https://doi.org/10.3322/CAAC.21492
• Brinton L, Gaudet M, Gierach G. (2018). Breast cancer. Thun M, Linet M, Cerhan J, Haiman C, Schottenfeld D, editors. Cancer Epidemiology and Prevention. New York: Oxford University Press, p. 861–88.
• Ceylan B, Özerdoğan N. (2015). Factors affecting age of onset of menopause and determination of quality of life in menopause. Turkish Journal of Obstetrics and Gynecology, 12(1), 43.-49. https://doi.org/10.4274/TJOD.79836
• Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, et al. (2014). Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Annals of Oncology : Official Journal of the European Society for Medical Oncology, 25(10), 1901–1914. https://doi.org/10.1093/ANNONC/MDU042
• Coughlin SS (2019). Social determinants of breast cancer risk, stage, and survival. Breast Cancer Research and Treatment, 177(3), 537–548. https://doi.org/10.1007/S10549-019-05340-7
• Ding W, Ruan G, Lin Y, Zhu J, Tu C, Li Z. (2021). Dynamic changes in marital status and survival in women with breast cancer: a population-based study. Scientific Reports, 11(1). https://doi.org/10.1038/S41598-021-84996-Y Dong JY, Qin LQ. (2020). Education level and breast cancer incidence: A meta-analysis of cohort studies. Menopause, 27(1), 113–118. https://doi.org/10.1097/GME.0000000000001425
• Feigelson HS, Bodelon C, Powers JD, Curtis RE, Buist DSM, Veiga LHS, et al. (2021). Body mass index and risk of second cancer among women with breast cancer. Journal of the National Cancer Institute, 113(9), 1156–1160. https://doi.org/10.1093/JNCI/DJAB053
• Ferrer RA, Portnoy D B, Klein WMP. (2013). Worry and risk perceptions as independent and interacting predictors of health protective behaviors. Journal of Health Communication, 18(4), 397–409. https://doi.org/10.1080/10810730.2012.727954
• Franzoi MA, Schwartsmann G, de Azevedo SJ, Geib G, Zaffaroni F, Liedke PER. (2019). Differences in breast cancer stage at diagnosis by ethnicity, ınsurance status, and family income in young women in the USA. Journal of Racial and Ethnic Health Disparities, 6(5), 909–916. https://doi.org/10.1007/S40615-019-00591-Y
• Gönül Y, Ulu Ş, Bucak A, Bilir A. (2015). Yapay sinir ağları ve klinik araştırmalarda kullanımı. Genel Tıp Dergisi, 25, 104–111.
• Güler B. (2022). Kadınların fiziksel aktiviteleri önündeki engeller: sistematik derleme çalışması. Spor Eğitim Dergisi, 6(1), 20–32. https://doi.org/10.55238/seder.1057239
• Hartman SJ, Dunsiger SI, Jacobsen PB. (2011). The relationship of psychosocial factors to mammograms, physical activity, and fruit and vegetable consumption among sisters of breast cancer patients. International Journal of Women’s Health, 3(1), 257–263. https://doi.org/10.2147/IJWH.S23246
• Jensen JD, Bernat JK, Davis LA, Yale R. (2010). Dispositional cancer worry: convergent, divergent, and predictive validity of existing scales. Journal of Psychosocial Oncology, 28(5), 470–489. https://doi.org/10.1080/07347332.2010.498459
• Jeong SH, An Y, Ahn, C, Park B, Lee MH, Noh DY, et al. (2020). Body mass index and risk of breast cancer molecular subtypes in Korean women: a case-control study. Breast Cancer Research and Treatment, 179(2), 459–470. https://doi.org/10.1007/S10549-019-0545 1-1
• Karagöz Y. (2017). SPSS ve AMOS uygulamalı, nitel – nicel – karma bilimsel araştırma yöntemleri ve yayın etiği. 1. Baskı, İstanbul, Nobel Yayıncılık, s. 56.
• Karakuş G, Öztürk Z, Tamam L. (2012). Ölüm ve ölüm kaygısı. Archives Medical Review Journal, 21(1),42-49.
• Katuwal S, Martinsen JI, Kjaerheim K, Sparen P, Tryggvadottir L, Lynge E, et al. (2018). Occupational variation in the risk of female breast cancer in the Nordic countries. Cancer Causes and Control, 29(11), 1027–1038. https://doi.org/10.1007/S10552-018-1076-2/FIGURES/3
• Keçeoğlu Ç, Gelbal S, Doğan N. (2016). ROC eğrisi yöntemi ile kesme puanının belirlenmesi. The Journal of Academic Social Science Studies, 9(50), 553–553. https://doi.org/10.9761/jasss3564
• Kizrak M, Bolat B. (2018). Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma. Bilişim Teknolojileri Dergisi, 11(3), 263–286.
• Lee JM, Lowry KP, Cott Chubiz JE, Swan J S, Motazedi T, Halpern EF, et al. (2020). Breast cancer risk, worry, and anxiety: effect on patient perceptions of false-positive screening results. Breast, 50, 104–112. https://doi.org/10.1016/j.breast.2020.02.004
• Lerman C, Trock B, Rimer BK, Jepson C, Brody D, Boyce A. (1991). Psychological side effects of breast cancer screening. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association, 10(4), 259–267. https://doi.org/10.1037/0278-6133.10.4.259
• Li C, Fan Z, Lin X, Cao M, Song F, Song F. (2021). Parity and risk of developing breast cancer according to tumor subtype: a systematic review and meta-analysis. Cancer Epidemiology, 75, 102050. https://doi.org/10.1016/J.CANEP.2021.102050
• Li M, Han M, Chen Z, Tang Y, Ma J, Zhang Z, et al. (2020). Does marital status correlate with the female breast cancer risk? A systematic review and meta-analysis of observational studies. PLoS ONE, 15(3). https://doi.org/10.1371/journal.pone.0229899
• Lök N, Ademli K. (2017). Yetişkin bireylerde fiziksel aktivite ve depresyon arasındaki ilişkinin belirlenmesi. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14(40), 101–110.
• Martínez ME, Unkart JT, Tao L, Kroenke CH, Schwab R, Komenaka I, et al. (2017). Prognostic significance of marital status in breast cancer survival: a population-based study. PloS ONE, 12(5), e0175515. https://doi.org/10.1371/JOURNAL.PONE.0175515
• Osei-Afriyie S, Addae A K, Oppong S, Amu H, Ampofo E,Osei E. (2021). Breast cancer awareness, risk factors and screening practices among future health professionals in Ghana: A cross-sectional study. PloS ONE, 16(6). https://doi.org/10.1371/JOURNAL.PONE.0253373
• Pacelli V, Azzollini M. (2011). An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications, 03(02), 103–112. https://doi.org/10.4236/jilsa.2011.32012
• Park S, Moon B I, Oh SJ, Lee H B, Seong MK, Lee S, et al. (2019). Clinical subtypes and prognosis in breast cancer according to parity: a nationwide study in Korean Breast Cancer Society. Breast Cancer Research and Treatment, 173(3), 679–691. https://doi.org/10.1007/S10549-018-5032-3
• Sari GN, Eshak ES, Shirai K, Fujino Y, Tamakoshi A, Iso H. (2020). Association of job category and occupational activity with breast cancer incidence in Japanese female workers: the JACC study. BMC Public Health, 20(1). https://doi.org/10.1186/S12889-020-09134-1
• Steindorf K, Ritte R, Eomois PP, Lukanova A, Tjonneland A, Johnsen NF, et al. (2013). Physical activity and risk of breast cancer overall and by hormone receptor status: the European prospective investigation into cancer and nutrition. International Journal of Cancer, 132(7), 1667–1678. https://doi.org/10.1002/IJC.27778
• Sullivan ES, Rice N, Kingston E, Kelly A, Reynolds JV, Feighan J, et al. (2021). A national survey of oncology survivors examining nutrition attitudes, problems and behaviours, and access to dietetic care throughout the cancer journey. Clinical Nutrition ESPEN, 41, 331–339. https://doi.org/10.1016/J.CLNESP.2020.10.023
• Timur Taşhan S, Uçar T, Aksoy Derya Y, Nacar G, Erci B. (2018). Validity and reliability of the Turkish version of the modified breast cancer worry scale. Iranian Journal of Public Health, 47(11), 1681–1687. Retrieved from https://ncbi.nlm.nih.gov /30581784/
• Ünal GS. (2018). Duygusal yeme ve obezite. Başkent Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, 2(2), 30–47.
• Wu Y, Zhang D, Kang S. (2013). Physical activity and risk of breast cancer: a meta-analysis of prospective studies. Breast Cancer Research and Treatment, 137(3), 869–882. https://doi.org/10.1007/S10549-012-2396-7/FIGURES/4
• Xie Z, Wenger N, Stanton AL, Sepucha K, Kaplan C, Madlensky L, et al. (2019). Risk estimation, anxiety, and breast cancer worry in women at risk for breast cancer: a single-arm trial of personalized risk communication. Psycho-Oncology, 28(11), 2226–2232. https://doi.org/10.1002/PON.5211
• Yılmaz B. (2019). Maliyet fonksiyonun belirlenmesinde yapay sinir ağı modellerinin kullanımı. Muhasebe ve Finansman Dergisi, Special Is, 329–344. https://doi.org/10.25095/MUFAD.607150