  Efficient early diagnosis is paramount in addressing the complexities of
Parkinson's disease because timely intervention can substantially mitigate
symptom progression and improve patient outcomes. In this paper, we present a
pioneering deep learning architecture tailored for the binary classification of
subjects, utilizing gait cycle datasets to facilitate early detection of
Parkinson's disease. Our model harnesses the power of 1D-Convolutional Neural
Networks (CNN), Gated Recurrent Units (GRU), and Graph Neural Network (GNN)
layers, synergistically capturing temporal dynamics and spatial relationships
within the data. In this work, 16 wearable sensors located at the end of
subjects' shoes for measuring the vertical Ground Reaction Force (vGRF) are
considered as the vertices of a graph, their adjacencies are modelled as edges
of this graph, and finally, the measured data of each sensor is considered as
the feature vector of its corresponding vertex. Therefore, The GNN layers can
extract the relations among these sensors by learning proper representations.
Regarding the dynamic nature of these measurements, GRU and CNN are used to
analyze them spatially and temporally and map them to an embedding space.
Remarkably, our proposed model achieves exceptional performance metrics,
boasting accuracy, precision, recall, and F1 score values of 99.51%, 99.57%,
99.71%, and 99.64%, respectively.
