  The DAMA/LIBRA observation of an annual modulation in the detection rate
compatible with that expected for dark matter particles from the galactic halo
has accumulated evidence for more than twenty years. It is the only hint of a
direct detection of the elusive dark matter, but it is in strong tension with
the negative results of other very sensitive experiments, requiring ad-hoc
scenarios to reconcile all the present experimental results. Testing the
DAMA/LIBRA result using the same target material, NaI(Tl), removes the
dependence on the particle and halo models and is the goal of the ANAIS-112
experiment, taking data at the Canfranc Underground Laboratory in Spain since
August 2017 with 112.5 kg of NaI(Tl). At very low energies, the detection rate
is dominated by non-bulk scintillation events and careful event selection is
mandatory. This article summarizes the efforts devoted to better characterize
and filter this contribution in ANAIS-112 data using a boosted decision tree
(BDT), trained for this goal with high efficiency. We report on the selection
of the training populations, the procedure to determine the optimal cut on the
BDT parameter, the estimate of the efficiencies for the selection of bulk
scintillation in the region of interest (ROI), and the evaluation of the
performance of this analysis with respect to the previous filtering. The
improvement achieved in background rejection in the ROI, but moreover, the
increase in detection efficiency, push the ANAIS-112 sensitivity to test the
DAMA/LIBRA annual modulation result around 3$\sigma$ with three-year exposure,
being possible to reach 5$\sigma$ by extending the data taking for a few more
years than the scheduled 5 years which were due in August 2022.
