Machine Learning Algorithms for Predicting High-Risk of Prostate Cancer Using Prostate-Specific Antigen (PSA), Age and Body Mass Index (BMI)

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. 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.