Classification of Breast Cancer Risk Factors Using Several Resampling Approaches

Md Faisal Kabir, Simone Ludwig

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Scopus citations

    Abstract

    Breast cancer is the most common cancer in women worldwide and the second most common cancer overall. Predicting the risk of breast cancer occurrence is an important challenge for clinical oncologists as it has direct influence in daily practice and clinical service. Classification is one of the supervised learning models that is applied in medical domains. Achieving better performance on real data that contains imbalance characteristics is a very challenging task. Machine learning researchers have been using various techniques to obtain higher accuracy, generally by correctly identifying majority class samples while ignoring the instances of the minority class. However, in most of the cases the concept of the minority class instances usually is of higher interest than the majority class. In this research, we applied three different classification techniques on a real world breast cancer risk factors data set. First, we applied specified classification techniques on breast cancer data without applying any resampling technique. Second, since the data is imbalanced meaning data has an unequal distribution between the classes, we applied several resampling methods to get better performance before applying the classifiers. The experimental results show significant improvement on using a resampling method as compared to applying no resampling technique, particularly for the minority class.

    Original languageEnglish (US)
    Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
    EditorsM. Arif Wani, Moamar Sayed-Mouchaweh, Edwin Lughofer, Joao Gama, Mehmed Kantardzic
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1243-1248
    Number of pages6
    ISBN (Electronic)9781538668047
    DOIs
    StatePublished - Jan 15 2019
    Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
    Duration: Dec 17 2018Dec 20 2018

    Publication series

    NameProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

    Conference

    Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
    Country/TerritoryUnited States
    CityOrlando
    Period12/17/1812/20/18

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Safety, Risk, Reliability and Quality
    • Signal Processing
    • Decision Sciences (miscellaneous)

    Fingerprint

    Dive into the research topics of 'Classification of Breast Cancer Risk Factors Using Several Resampling Approaches'. Together they form a unique fingerprint.

    Cite this