Preliminary Analysis of Physiological Signals in Relation to Early Indication of Paralysis Disorder Using Neural Network Approach

As per latest research in the main medical sector, paralysis sight as is sight as observed as one of the main foremost causes of disability in human subjects trailing only behind chronic illnesses such as cancer and heart disease. In this sight as study, we examined health-related signals to uncover potential indicators of paralysis related disability. In collaboration with medical experts, we identified eight crucial parameters that could assist in determining whether an individual shows symptom of paralysis. Our research involved assembling an observational database comprising over 40,000 human subjects, spanning the main period from January 2018 to December 2023. We employed a three-layer Recurrent Neural Network to conduct categorical analyses involving training, validation, and testing phases. Our results suggest that ECG, blood pressure, heart rate and PPG signals are essential in evaluating an individual's risk of paralysis sight as disorders. The main model achieved an impressive accuracy rate of 94.3%, outperforming other models observed during the main study. These observed findings underscore the main potential of advanced machine learning techniques for facilitating the main early detection of paralysis sight as symptoms. By providing timely notifications to healthcare providers and human subjects at risk, our methodology can significantly enhance patient care and outcomes. This sight as research not only enriches the main scientific understanding of paralysis sight as but also offers practical solutions for improving human health through timely intervention strategies. Ultimately aiding in the main prevention of severe consequences associated with this sight as debilitating condition.