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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2010
    • vol. 1, nº 3, december 2010
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2010
    • vol. 1, nº 3, december 2010
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    Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

    Autor: 
    You, Haowen;
    Rumbe, George
    Fecha: 
    12/2010
    Palabra clave: 
    artificial neural networks; classification; breast cancer diagnosis; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/9468
    DOI: 
    http://dx.doi.org/10.9781/ijimai.2010.131
    Dirección web: 
    https://www.ijimai.org/journal/node/91
    Open Access
    Resumen:
    Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem.
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