Prostate-specific antigen (PSA)-based screening for prostate cancer has been widely performed, but its accuracy is unsatisfactory. To improve accuracy, building an effective statistical model using machine learning methods (MLMs) is a promising approach.Data on continuous changes in the PSA level over the past 2 years were accumulated from 512 patients who underwent prostate biopsy after PSA screening. The age of the patients, PSA level, prostate volumes, and white blood cell count in urinalysis were used as input data for the MLMs. As MLMs, we evaluated the efficacy of three different techniques: artificial neural networks (ANNs), random forest, and support vector machine. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and compared with the PSA level and the conventional PSA-based parameters: PSA density and PSA velocity.When using two annual PSA testing, all receiver operating characteristic curves of the three MLMs were above the curve for the PSA level, PSA density, and PSA velocity. The AUCs of ANNs, random forest, and support vector machine were 0.69, 0.64, and 0.63, respectively. Those values were higher than the AUCs of the PSA level, PSA density, and PSA velocity, 0.53, 0.41, and 0.55, respectively. The accuracies of the MLMs (71.6% to 72.1%) were also superior to those of the PSA level (39.1%), PSA density (49.7%), and PSA velocity (54.9%). Among the MLMs, ANNs showed the most favorable AUC. The MLMs showed higher sensitivity and specificity than conventional PSA-based parameters. The model performance did not improve when using three annual PSA testing.The present retrospective study results indicate that machine learning techniques can predict prostate cancer with significantly better AUCs than those of PSA density and PSA velocity.