Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review

Siti utami dewi; Rr. Tutik Sri Haryati

doi:10.46749/jiko.v5i2.70

Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review

Siti utami dewi, Rr. Tutik Sri Haryati

Abstract

Breast cancer is one of the most common cancer causes of death in sufferers. Breast cancer is the second leading cause of death for Indonesian women. For this reason, a device that can identify the risk of breast cancer is needed. The fuzzy method with the Mamdani model is one method that has been widely used in software development to determine the level of breast cancer risk. Purpose: To know the software development of the Fuzzy Inference System Method with the Mamdani model to identify the level of breast cancer risk. Methods: This research is a literature study with the data collection process through the PubMed, Science Direct, ProQuest, and Google Scholar databases. The criteria for the articles used are 2010-2021 publications. Results: Based on a review of 10 journals using the Fuzzy Inference System (FIS) Mamdani model can provide effective results to identify risks for people who have the possibility of developing breast cancer. Conclusion: The fuzzy expert system using the Mamdani method can be applied to the problem domain of breast cancer diagnosis with a fairly good level of accuracy to be able to overcome the inequality of mammography results.

Full Text:

PDF

References

Atashi, A., Nazeri, N., Abbasi, E., Dorri, S., & Alijani-Z, M. (2017). Breast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm. Multidisciplinary Cancer Investigation, 1(2), 20–26. https://doi.org/10.21859/mci-01029

Bridget O, M. (2018). Design and Implementation of a Mobile Based Fuzzy Expert System for Pre Breast Cancer Growth Prognosis. International Journal of Advanced Research in Computer Science, 9(3), 258–279. https://doi.org/10.26483/ijarcs.v9i3.6116

Dudek, G., Strzelewicz, A., Krasowska, M., Rybak, A., & Turczyn, R. (2012). Fuzzy analysis of the cancer risk factor. Acta Physica Polonica B, 43(5), 947–959. https://doi.org/10.5506/APhysPolB.43.947

Haznedar B., Arslan, M.T., & Kalinli A. (2018). Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles. Tamap Journal of Engineering, 2018(1). https://doi.org/10.29371/2018.3.29

Harbeck, Nadia & Penault-Llorca, Frédérique & Cortes, Javier & Gnant, Michael & Houssami, Nehmat & Poortmans, Philip & Ruddy, Kathryn & Tsang, Janice & Cardoso, Fatima. (2019). Breast cancer. Nature Reviews Disease Primers. 5. 10.1038/s41572-019-0111-2.

Huang, M. L., Hung, Y. H., Lee, W. M., Li, R. K., & Wang, T. H. (2012). Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. Journal of Medical Systems, 36(2), 407–414. https://doi.org/10.1007/s10916-010-9485-0

Keles, A., A. Keles, and U. Yavuz. (2011). Expert system based on neuro-fuzzy rules for diagnosis breast cancer. [doi: 10.1016/j.eswa.2010.10.061]. Expert Systems with Applications, 38(5), 5719-5726.

Kemenkes RI. (2013). Riset Kesehatan Dasar; RISKESDAS. Jakarta: Balitbang Kemenkes RI

Khezri, R., Hosseini, R., Mazinani, M. (2014). A fuzzy Rule-based Expert System for the Risk of Development of the Breast Cancer. International Journal of Engineering. Vol.27, No.10

Kusumadewi, Sri dan Purnomo Hari. (2010). Aplikasi Logika Fuzzy, Cetakan Pertama, Yogyakarta: Graham Ilmu

Miranda, G. H. B., & Felipe, J. C. (2015). Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Computers in Biology and Medicine, 64, 334–346. https://doi.org/10.1016/j.compbiomed.2014.10.006

Normalisa, N. (2018). Fuzzy Inference System dengan Metode Mamdani untuk Prediksi Kanker Payudara. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 1(1), 37. https://doi.org/10.32493/jtsi.v1i1.1951

Saleh, A.A.E., Sherif E.B., Ahmed A.E.A., March. (2011)., A Fuzzy Decision Support System for Management of Breast Cancer, International Journal of Advanced Computer Science and Applications,Vol. 2, No.3.

Wang, Lulu. (2017). Early Diagnosis of Breast Cancer. Sensors. 17. 1572. 10.3390/s17071572.

Shleeg, A. A., & Ellabib, I. M. (2013). Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 7(10), 1343–1347.

Shoumi, MN, Syulistyo. (2021). Analisis resiko kanker payudara (Breast Cancer) menggunakan fuzzy Inference system (FIS) model Mamdani. Jurnal Informasi Interaktif Vol. 6 No.1

Sizilio, G. R. M. A., Leite, C. R. M., Guerreiro, A. M. G., & Neto, A. D. D. (2012). Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis. BioMedical Engineering Online, 11, 1–22. https://doi.org/10.1186/1475-925X-11-83

Yilmaz, A., & Ayan, K. (2011). Risk analysis in breast cancer disease by using fuzzy logic and effects of stress level on cancer risk. Scientific Research and Essays, 6(24), 5179–5191. https://doi.org/10.5897/SRE11.840

DOI: https://doi.org/10.46749/jiko.v5i2.70