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On the Breast Mass Diagnosis Using Bayesian Networks

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Nature-Inspired Computation and Machine Learning (MICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8857))

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Abstract

Nowadays, breast cancer is considered a significant health problem in Mexico. Mammogram is an effective study for detecting mass lesions, which could indicate this disease. However, due to the density of breast tissue and a wide range of mass characteristic, the mass diagnosis is difficult. In this study, the performance comparison of Bayesian networks models on classification of benign and malignant masses is presented. Here, Naïve Bayes, Tree Augmented Naïve Bayes, K-dependence Bayesian classifier, and Forest Augmented Naïve Bayes models are analyzed. Two data sets extracted from the public BCDR-F01 database, including 112 benign and 119 malignant masses, were used to train the models. The experimental results have shown that TAN, KDB, and FAN models with a subset of only eight features have achieved a performance of 0.79 in accuracy, 0.80 in sensitivity, and 0.77 in specificity. Therefore, these models which allow dependencies among variables (features), are considered as suitable and promising methods for automated mass classification.

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Authors and Affiliations

  1. Universidad Tecnológica de la Mixteca, 69000, Huajuapan de León, Oaxaca, México

    Verónica Rodríguez-López & Raúl Cruz-Barbosa

Authors
  1. Verónica Rodríguez-López
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  2. Raúl Cruz-Barbosa
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  2. Universidad Autónoma del Estado de Hidalgo, Área Académica de Computación y Electrónica, Carretera Pachuca-Tulancingo, , Km. 4.5, Col. Carboneras, Mineral de la Reforma,, 42180, Hidalgo, Mexico

    Félix Castro Espinoza

  3. Facultad de ciencias, Universidad Autónoma Nacional de México, Ciudad Universitaria, México DF, Mexico

    Sofía N. Galicia-Haro

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Rodríguez-López, V., Cruz-Barbosa, R. (2014). On the Breast Mass Diagnosis Using Bayesian Networks. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_41

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  • DOI: https://doi.org/10.1007/978-3-319-13650-9_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13649-3

  • Online ISBN: 978-3-319-13650-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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