In this research, a novel approach to the clinical condition of neurodegenerative disorders like Parkinson's is presented. The proposed method uses a mix of deep networks that have been pre-trained as well as long-term and short-term memory (LSTM). A new model called PD-ResNet is constructed and based on the residual network (ResNet) architecture to understand the variations between people with Parkinson disease and healthy controls. In order to execute adoption of the obtained learnt representations across data originating from various medical contexts, a new loss functionality is presented as well as used in the development of the deep neural networks (DNNs). Experiments conducted on the clinic gait dataset demonstrate that our suggested model has good performance, with a correctness of 95.51%, an accuracy of 94.44%, a recalls of 96.59%, a sensitivity of 94.44%, as well as a F1 measure of 95.50%.
Gokuldhev et al. (2026) studied this question.