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Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset

Author

Listed:
  • Yamid Fabián Hernández-Julio

    (Faculty of Economics, Administrative and Accounting Sciences, Universidad del Sinú Elías Bechara Zainúm, Montería 230002, Colombia)

  • Leonardo Antonio Díaz-Pertuz

    (Faculty of Economics, Administrative and Accounting Sciences, Universidad del Sinú Elías Bechara Zainúm, Montería 230002, Colombia)

  • Martha Janeth Prieto-Guevara

    (Departamento de Ciencias Acuícolas–Medicina Veterinaria y Zootecnia (CINPIC), Universidad de Córdoba, Montería 230002, Colombia)

  • Mauricio Andrés Barrios-Barrios

    (Systems Engineering Department, Universidad de la Costa, Barranquilla 080001, Colombia)

  • Wilson Nieto-Bernal

    (Facultad de Ingeniería, Departamento de Ingeniería de Sistemas, Universidad del Norte, Barranquilla 80001, Colombia)

Abstract

Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance’ metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems—FIS, demonstrating superior precision.

Suggested Citation

  • Yamid Fabián Hernández-Julio & Leonardo Antonio Díaz-Pertuz & Martha Janeth Prieto-Guevara & Mauricio Andrés Barrios-Barrios & Wilson Nieto-Bernal, 2023. "Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset," IJERPH, MDPI, vol. 20(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:6:p:5103-:d:1096648
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