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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/63340

    Título
    Breast cancer prediction using fine needle aspiration features and upsampling with supervised machine learning
    Autor
    Shafique, Rahman
    Rustam, Furqan
    Choi, Gyu Sang
    Torre Díez, Isabel de laAutoridad UVA
    Mahmood, Arif
    Lipari, Vivian
    Rodríguez Velasco, Carmen Lili
    Ashraf, Imran
    Año del Documento
    2023
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Cancers, 2023, Vol. 15, Nº. 3, 681
    Résumé
    Simple Summary: Breast cancer is prevalent in women and the second leading cause of death. Conventional breast cancer detection methods require several laboratory tests and medical experts. Automated breast cancer detection is thus very important for timely treatment. This study explores the influence of various feature selection technique to increase the performance of machine learning methods for breast cancer detection. Experimental results shows that use of appropriate features tend to show highly accurate prediction.
     
    Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.
    Materias (normalizadas)
    Breast - Cancer - Diagnosis
    Cancer research
    Breast - Diseases
    Mamas - Cáncer
    Cytology
    Mamas - Citología
    Principal components analysis
    Análisis multivariante
    Singular value decomposition
    Machine learning
    Aprendizaje automático
    Artificial intelligence
    Oncology
    Materias Unesco
    3207.13 Oncología
    1203.04 Inteligencia Artificial
    ISSN
    2072-6694
    Revisión por pares
    SI
    DOI
    10.3390/cancers15030681
    Version del Editor
    https://www.mdpi.com/2072-6694/15/3/681
    Propietario de los Derechos
    © 2023 The authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/63340
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Artículos de revista [358]
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    Breast-Cancer-Prediction.pdf
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    4.793Mo
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    Atribución 4.0 InternacionalExcepté là où spécifié autrement, la license de ce document est décrite en tant que Atribución 4.0 Internacional

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