Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico.
Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Ciudad de México 07700, Mexico.
Int J Environ Res Public Health. 2023 Feb 13;20(4):3240. doi: 10.3390/ijerph20043240.
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.
癌症的早期诊断已经从多个角度进行了研究，因此必须继续改进分类算法，以实现疾病的早期诊断和提高患者的生存率。在医学领域，由于各种原因，存在数据丢失的情况。也有一些数据集混合了数值和类别值。很少有算法可以对具有此类特征的数据集进行分类。因此，本研究提出了对现有的癌症分类算法进行修改。与经典的分类算法相比，该算法显示出了优异的结果。AISAC-MMD（混合和缺失数据）基于 AISAC，并进行了修改，以便与具有缺失值和混合值的数据集一起使用。它的性能明显优于生物启发或经典分类算法。统计分析表明，AISAC-MMD 在进行乳腺癌分类时，明显优于最近邻、C4.5、朴素贝叶斯、ALVOT、朴素关联分类器、AIRS1、Immunos1 和 CLONALG 算法。
