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A Performance Comparison on the Machine Learning Classifiers in Predictive Pathology Staging of Prostate Cancer

This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.