Identifying high-risk breast cancer patients using microarray and clinical data

Azni Nasuha Ngisa, Ong Huey Fang

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


    The performance of DNA microarray in breast cancer prediction demonstrates the potential of genome-wide analysis using gene expression data. Therefore, this study proposed a prediction method called GridPCA to identify highrisk breast cancer patients using both microarray and clinical data. The GridSearch and Principal Component Analysis are employed in the proposed method to deal with the high dimensionality of microarray data. The experimental results showed that GridPCA achieved approximately 82% of average predictive accuracy with Decision Tree, K-Nearest Neighbour, Logistic Regression and Support Vector Machine classifiers. In future, the proposed method could be used in developing systems that help doctors in planning, decision making and tailoring appropriate treatments for increasing the survival rate of breast cancer patients.

    Original languageEnglish
    Title of host publication2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020)
    EditorsTaesung Park, Young-Rae Cho, Xiaohua Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
    Place of PublicationPiscataway NJ USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages5
    ISBN (Electronic)9781728162157
    ISBN (Print)9781728162164
    Publication statusPublished - 2020
    EventIEEE International Conference on Bioinformatics and Biomedicine 2020 - Virtual, Seoul, Korea, Republic of (South)
    Duration: 16 Dec 202019 Dec 2020 (Proceedings)


    ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2020
    Abbreviated titleBIBM 2020
    Country/TerritoryKorea, Republic of (South)
    CityVirtual, Seoul
    Internet address


    • breast cancer
    • clinical data
    • microarray
    • prediction

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