Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

  • Fabio Cardoso Pontifícia Universidade Católica do Rio de Janeiro
  • Thiago Medeiros Pontifícia Universidade Católica do Rio de Janeiro
  • Marley Vellasco Pontifícia Universidade Católica do Rio de Janeiro
  • Karla Figueiredo Universidade do Estado do Rio de Janeiro

Resumo


Breast cancer is the most common cancer diagnosed in the world, being the cause of death of 685,000 people worldwide in 2020. Due to the aggressiveness of the disease, early-stage identification, treatment, and remission detection are important to ensure longevity to those who may have cancer. In this paper, we propose a fuzzy-genetic approach for breast cancer recurrence classification. To this end, we use a Genetic Algorithm to design automatically the fuzzy inference system with the objective of balancing between accuracy and explainability. The proposed system achieved an accuracy of 91.30%, finding eleven rules with a maximum of three antecedents per rule, which provided a competitive result compared to other Machine Learning approaches.

Palavras-chave: Fuzzy System, Genetic Algorithm, Classification, Breast Cancer

Referências

Anderson, B.O., Ilbawi, A.M., Fidarova, E., Weiderpass, E., Stevens, L., Abdel-Wahab, M., Mikkelsen, B.: The global breast cancer initiative: a strategic collaboration to strengthen health care for non-communicable diseases. The Lancet Oncology 22(5), 578–581 (2021)

Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., Vignat, J., Gralow, J.R., Cardoso, F., Siesling, S., et al.: Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast 66, 15–23 (2022)

Boadh, R., Aarya, D.D., Dahiya, M., Rathee, R., Rathee, S., Kumar, A., Jain, S., Rajoria, Y.K.: Study and prediction of prostate cancer using fuzzy inference system. Materials Today: Proceedings 56, 157–164 (2022). https://doi.org/10.1016/j.matpr.2022.01.040, [link], international Conference on Materials, Machines and Information Technology-2022

Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (April 2002). https://doi.org/10.1109/4235.996017

Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning (2017)

Erdem, E., Bozkurt, F.: Prostat kanseri tahmini için çeşitli denetimli makine öğrenimi tekniklerinin kar şıla ştırılması (2021)

Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181(20), 4340–4360 (2011)

Gonçalves, C.B., Souza, J.R., Fernandes, H.: Cnn architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Computers in Biology and Medicine 142, 105205 (2022)

Gravina, M., Spirito, L., Celentano, G., Capece, M., Creta, M., Califano, G., Collà Ruvolo, C., Morra, S., Imbriaco, M., Di Bello, F., Sciuto, A., Cuocolo, R., Napolitano, L., La Rocca, R., Mirone, V., Sansone, C., Longo, N.: Machine learning and clinical-radiological characteristics for the classification of prostate cancer in pi-rads 3 lesions. Diagnostics 12(7) (2022). https://doi.org/10.3390/diagnostics12071565, [link]

Gupta, S., Kumar, Y.: Cancer prognosis using artificial intelligence-based techniques. SN Computer Science 3(1), 77 (Nov 2021). https://doi.org/10.1007/s42979-021-00964-3.

Gupta, V., Gaur, H., Vashishtha, S., Das, U., Singh, V.K., Hemanth, D.J.: A fuzzy rule-based system with decision tree for breast cancer detection (Mar 2023). https://doi.org/10.1049/ipr2.12774

Heer, E., Harper, A., Escandor, N., Sung, H., McCormack, V., Fidler-Benaoudia, M.M.: Global burden and trends in premenopausal and postmenopausal breast cancer: a population-based study. The Lancet Global Health 8(8), e1027–e1037 (2020)

Janssen, F.M., Aben, K.K.H., Heesterman, B.L., Voorham, Q.J.M., Seegers, P.A., Moncada-Torres, A.: Using explainable machine learning to explore the impact of synoptic reporting on prostate cancer. Algorithms 15(2) (2022). https://doi.org/10.3390/a15020049, [link]

Kant, S., Agarwal, D., Shukla, P.K.: A survey on fuzzy systems optimization using evolutionary algorithms and swarm intelligence. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds.) Computer Vision and Robotics. pp. 421–444. Springer Singapore, Singapore (2022)

Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017)

Maqsood, S., Damaˇseviˇcius, R., Maskeliu ̄nas, R.: Ttcnn: A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages. Applied Sciences 12(7), 3273 (2022)

Mencar, C., Lucarelli, M., Castiello, C., Maria, F.A.: Design of strong fuzzy partitions from cuts. In: 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13). pp. 464–471. Atlantis Press (2013)

Ngatchou, P., Zarei, A., El-Sharkawi, A.: Pareto multi objective optimization. In: Proceedings of the 13th international conference on, intelligent systems application to power systems. pp. 84–91. IEEE (2005)

Ouifak, H., Idri, A.: On the performance and interpretability of mamdani and takagi-sugeno-kang based neuro-fuzzy systems for medical diagnosis. Scientific African 20, e01610 (2023)

Ravale, U., Bendale, Y.: Breast cancer prediction using different machine learning algorithm. In: Shakya, S., Du, K.L., Ntalianis, K. (eds.) Sentiment Analysis and Deep Learning. pp. 493–502. Springer Nature Singapore, Singapore (2023)

Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.: Cancer statistics, 2023. CA: a cancer journal for clinicians 73(1), 17–48 (2023)

Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. pp. 180–186 (2020)

Szili, F.A., Botzheim, J., Nagy, B.: Bacterial evolutionary algorithm-trained interpolative fuzzy system for mobile robot navigation. Electronics 11(11) (2022). https://doi.org/10.3390/electronics11111734, [link]

Tang, K.S., Man, K.F., Liu, Z.F., Kwong, S.: Minimal fuzzy memberships and rules using hierarchical genetic algorithms. IEEE Transactions on Industrial Electronics 45(1), 162–169 (Feb 1998). https://doi.org/10.1109/41.661317

Thimoteo, L.M., Vellasco, M.M., Amaral, J., Figueiredo, K., Yokoyama, C.L., Marques, E.: Explainable artificial intelligence for covid-19 diagnosis through blood test variables. Journal of Control, Automation and Electrical Systems 33(2), 625–644 (2022)
Publicado
25/09/2023
CARDOSO, Fabio; MEDEIROS, Thiago; VELLASCO, Marley; FIGUEIREDO, Karla. Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 447-460. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.234253.

Artigos mais lidos do(s) mesmo(s) autor(es)