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A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

Author

Listed:
  • David González-Patiño

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico)

  • Yenny Villuendas-Rey

    (Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Ciudad de México 07700, Mexico)

  • Magdalena Saldaña-Pérez

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico)

  • Amadeo-José Argüelles-Cruz

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico)

Abstract

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

Suggested Citation

  • David González-Patiño & Yenny Villuendas-Rey & Magdalena Saldaña-Pérez & Amadeo-José Argüelles-Cruz, 2023. "A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification," IJERPH, MDPI, vol. 20(4), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3240-:d:1066410
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    References listed on IDEAS

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    1. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
    2. Jorge E. Mendoza & Louis-Martin Rousseau & Juan G. Villegas, 2016. "A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints," Journal of Heuristics, Springer, vol. 22(4), pages 539-566, August.
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