Multi-parametric MRI (mpMRI) can be used to non-invasively predict the presence of a Gleason 4 pattern in transition zone (TZ) and peripheral zone (PZ) prostate cancers. Here the performance of five machine-learning classifiers, which use mpMRI and clinical features, were compared. Analysis included a five-fold cross validation and a temporally separated validation to prove the generalisability of the classifiers. The results showed that PZ models can predict the presence of a Gleason 4 pattern better than TZ models. The statistically better PZ classifier is a linear regression model while for TZ the best classifier is Naïve Bayes model.