Abstract
Serum Prostate Specific Antigen (PSA) level is used for prediction of cancer, but this approach suffers from weak sensitivity and specificity. We applied binary-split decision tree (DT) algorithm to prostate cancer prediction problem. Materials and methods: Quick, Unbiased and Efficient Statistical Tree (QUEST) algorithm was used in 750 patients who had a serum PSA levels between 0 and 10 ng/mL. Results: The analysis indicated that following five nodes had different levels of cancer possibility: (1) PSA > 5.98 ng/mL; (2) PSA 0.81; (4) PSA Keywords:
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