JOURNAL ARTICLE

Predicción de cáncer de mama a través de biomarcadores mediante aprendizaje automático

Andrea QuintanillaNicole Mancilla MedinaJosé Sulla-Torres

Year: 2020 Journal:   Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integration, And Alliances for A Sustainable Development” “Hemispheric Cooperation for Competitiveness and Prosperity on A Knowledge-Based Economy”

Abstract

The prediction of breast cancer through biomarkers is proposed through machine learning, in order to minimize the waiting time that exists at the time a cancer is discarded due to the different factors that exist in our national reality. For this, a Neural Network has been used, which is an Automatic Learning algorithm that allowed us to make the prediction. The results showed that with the developed design of the Neural Network an accuracy of 82.76% was obtained, likewise, a prototype was built that allowed validating the proposal, with which it can be concluded that the Neural Network is an adequate algorithm to be used for Complementary to the prediction of breast cancer through biomarkers and that the developed prototype serves those interested in the oncology field.

Keywords:
Computer science

Metrics

2
Cited By
0.62
FWCI (Field Weighted Citation Impact)
14
Refs
0.66
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Genetics, Bioinformatics, and Biomedical Research
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology
Biomedical and Engineering Education
Physical Sciences →  Engineering →  Biomedical Engineering
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