  This paper introduces BagStacking, a novel ensemble learning method designed
to enhance the detection of Freezing of Gait (FOG) in Parkinson's Disease (PD)
by using a lower-back sensor to track acceleration. Building on the principles
of bagging and stacking, BagStacking aims to achieve the variance reduction
benefit of bagging's bootstrap sampling while also learning sophisticated
blending through stacking. The method involves training a set of base models on
bootstrap samples from the training data, followed by a meta-learner trained on
the base model outputs and true labels to find an optimal aggregation scheme.
The experimental evaluation demonstrates significant improvements over other
state-of-the-art machine learning methods on the validation set. Specifically,
BagStacking achieved a MAP score of 0.306, outperforming LightGBM (0.234) and
classic Stacking (0.286). Additionally, the run-time of BagStacking was
measured at 3828 seconds, illustrating an efficient approach compared to
Regular Stacking's 8350 seconds. BagStacking presents a promising direction for
handling the inherent variability in FOG detection data, offering a robust and
scalable solution to improve patient care in PD.
