  Solar Energetic Particle events (SEPs) are among the most dangerous transient
phenomena of solar activity. As hazardous radiation, SEPs may affect the health
of astronauts in outer space and adversely impact current and future space
exploration. In this paper, we consider the problem of daily prediction of
Solar Proton Events (SPEs) based on the characteristics of the magnetic fields
in solar Active Regions (ARs), preceding soft X-ray and proton fluxes, and
statistics of solar radio bursts. The machine learning (ML) algorithm uses an
artificial neural network of custom architecture designed for whole-Sun input.
The predictions of the ML model are compared with the SWPC NOAA operational
forecasts of SPEs. Our preliminary results indicate that 1) for the AR-based
predictions, it is necessary to take into account ARs at the western limb and
on the far side of the Sun; 2) characteristics of the preceding proton flux
represent the most valuable input for prediction; 3) daily median
characteristics of ARs and the counts of type II, III, and IV radio bursts may
be excluded from the forecast without performance loss; and 4) ML-based
forecasts outperform SWPC NOAA forecasts in situations in which missing SPE
events is very undesirable. The introduced approach indicates the possibility
of developing robust "all-clear" SPE forecasts by employing machine learning
methods.
