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Prediction of Breast Cancer Diagnosis by Blood Biomarkers Using Artificial Neural Networks

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VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering (CLAIB 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 75))

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Abstract

The use of artificial neural networks (ANNs) has been very helpful in carrying out prediction, classification and data optimization tasks. In this work, ANNs were used to predict the diagnosis for breast cancer in a women population with overweight or obese and possible diabetes, coupled with a pre or postmenopausal stage and compared against other machine learning techniques reported in literature. The algorithms used to train the ANNs models were Scaled Conjugate Gradient, Resilient Backpropagation and Conjugate Gradient Backpropagation with Powell Beale Restarts. The algorithms results were compared with the original dataset creator’s work, as well as other authors using the same dataset for classification task, a better classification was accomplished using this work ANNs. With four predictors the next values were obtained, AUC = 0.96, sensitivity = 0.96, specificity = 0.96 and Youden index = 0.92. With nine predictors the next values were obtained, AUC = 0.96, sensitivity = 0.95, specificity = 0.97 and Youden index = 0.92. Different strategies are suggested to improve the results, exploring more hidden layers and a different validation method.

R. Castañeda—Independent researcher.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, 22860, Ensenada, Baja California, Mexico

    Balam Benítez-Mata, Carlos Castro, Eunice Vargas & Dora-Luz Flores

  2. Ensenada, Baja California, Mexico

    Rubén Castañeda

Authors
  1. Balam Benítez-Mata
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  2. Carlos Castro
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  3. Rubén Castañeda
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  4. Eunice Vargas
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  5. Dora-Luz Flores
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Corresponding author

Correspondence to Balam Benítez-Mata .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    César A. González Díaz

  2. Departamento de Ingeniería Electrica y Computación, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Christian Chapa González

  3. Universidad Nacional de San Juan, San Juan, Argentina

    Eric Laciar Leber

  4. Universidad de Guadalajara, Guadalajara, Mexico

    Hugo A. Vélez

  5. Universidad Autónoma de Nuevo León, Nuevo León, Mexico

    Norma P. Puente

  6. Universidad Autónoma de Baja California, Ensenada, Baja California, Mexico

    Dora-Luz Flores

  7. Universidade Federal de Uberlândia, Uberlândia, Brazil

    Adriano O. Andrade

  8. Instituto Nacional de Cancerología, Mexico City, Mexico

    Héctor A. Galván

  9. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Fabiola Martínez

  10. Universidad Federal de Santa Catarina, Florianópolis, Brazil

    Renato García

  11. Instituto Nacional de Rehabilitación, Mexico City, Mexico

    Citlalli J. Trujillo

  12. Universidad Autónoma de San Luis Potos, San Luis, Mexico

    Aldo R. Mejía

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Benítez-Mata, B., Castro, C., Castañeda, R., Vargas, E., Flores, DL. (2020). Prediction of Breast Cancer Diagnosis by Blood Biomarkers Using Artificial Neural Networks. In: González Díaz, C., et al. VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-30648-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-30648-9_7

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