The prediction of solar energetic particle (SEP) events or solar radiation storms is one of the most important problems in the space weather field. These events may have adverse effects on technology infrastructures and humans in space; they may also irradiate passengers and flight crews in commercial aircraft flying at polar latitudes. This paper explores the use of ≥ M2 solar flares and radio burst observations as proxies for predicting >10 MeV SEP events on Earth. These observations are manifestations of the parent event at the sun associated with the SEP event. As a consequence of processing data at the beginning of the physical process that leads to the radiation storm, the model may provide its predictions with large anticipation. The main advantage of the present approach is that the model analyzes solar data that are updated every 30 min and, as such, it may be operational; however, a disadvantage is that those SEP events associated with strong well-connected flares cannot be predicted. For the period from November 1997 to February 2014, we obtained a probability of detection of 70.2%, a false alarm ratio of 40.2%, and an average anticipation time of 9 h 52 min. In this study, the prediction model was built using decision trees, an interpretable machine learning technique. This approach leads to outputs and results comparable to those derived by the Empirical model for Solar Proton Event Real Time Alert (ESPERTA) model. The obtained decision tree shows that the best criteria to differentiate pre-SEP scenarios and non-pre-SEP scenarios are the peak and integrated flux for soft X-ray flares and the radio type III bursts.