Prediction of disruptions from scratch is an ITER relevant topic. The first operations with the new ITER-like wall constitute a good opportunity to test the development of new predictors from scratch and the related methodologies. These methodologies have been based on the Advanced Predictor Of DISruptions (APODIS) architecture. APODIS is a real-time disruption predictor that is in operation in the JET real-time network. Balanced and unbalanced datasets have been used to develop realtime predictors from scratch. The discharges have been used in chronological order. Also, different criteria to decide when to re-train a predictor are discussed. Best results are obtained by applying a hybrid method (balanced/unbalanced datasets) for training and with the criterion of re-training after every missed alarm. The predictors have been tested off-line with all the discharges (disruptive/ non disruptive) corresponding to the first three JET ITER-like wall campaigns. The results give asuccess rate of 93.8% and a false alarm rate of 2.8%. It should be considered that these results are obtained from models trained with no more than 42 disruptive discharges.