Predicting High-Risk Prostate Cancer Using Machine Learning Methods

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 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.