TY - JOUR
T1 - A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification
AU - González-Patiño, David
AU - Villuendas-Rey, Yenny
AU - Saldaña-Pérez, Magdalena
AU - Argüelles-Cruz, Amadeo José
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - bio-inspired algorithms
KW - breast cancer
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85148964992&partnerID=8YFLogxK
U2 - 10.3390/ijerph20043240
DO - 10.3390/ijerph20043240
M3 - Artículo
C2 - 36833936
AN - SCOPUS:85148964992
SN - 1661-7827
VL - 20
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 4
M1 - 3240
ER -