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

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... te 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 n predicting high-risk prostate cancer. We evaluated the contribution of rate of change of PSA, age, and BMI to this model's accuracy. We identified that including the rate of change of PSA and age n our model increased the area under the curve (AUC) of the model by 6.8%, whereas BMI had a minimal effect.