IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v4y2019i3p129-d263430.html
   My bibliography  Save this article

Predicting High-Risk Prostate Cancer Using Machine Learning Methods

Author

Listed:
  • Henry Barlow

    (School of Computer Science, University of Sydney, 2006 Sydney, Australia)

  • Shunqi Mao

    (School of Computer Science, University of Sydney, 2006 Sydney, Australia)

  • Matloob Khushi

    (School of Computer Science, University of Sydney, 2006 Sydney, Australia)

Abstract

Prostate cancer can be low- or high-risk to the patient’s health. Current screening on the basis of prostate-specific antigen (PSA) levels has a tendency towards both false positives and false negatives, both of which have negative consequences. We obtained a dataset of 35,875 patients from the screening arm of the National Cancer Institute’s Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. We segmented the data into instances without prostate cancer, instances with low-risk prostate cancer, and instances with high-risk prostate cancer. We developed a pipeline to deal with imbalanced data and proposed algorithms to perform preprocessing on such datasets. We evaluated the accuracy of various machine learning algorithms in predicting high-risk prostate cancer. An accuracy of 91.5% can be achieved by the proposed pipeline, using standard scaling, SVMSMOTE sampling method, and AdaBoost for machine learning. We then evaluated the contribution of rate of change of PSA, age, BMI, and filtration by race to this model’s accuracy. We identified that including the rate of change of PSA and age in our model increased the area under the curve (AUC) of the model by 6.8%, whereas BMI and race had a minimal effect.

Suggested Citation

  • Henry Barlow & Shunqi Mao & Matloob Khushi, 2019. "Predicting High-Risk Prostate Cancer Using Machine Learning Methods," Data, MDPI, vol. 4(3), pages 1-15, September.
  • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:129-:d:263430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/4/3/129/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/4/3/129/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:129-:d:263430. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.