Stroke Risk Stratification Using Neural Networks

Current primary stroke preventive strategies seem insufficient in light of the increased prevalence of stroke, the steady or increasing death rates from cardiovascular illnesses, and the expanding list of stroke risk factors. A class of computer algorithms known as machine learning (ML) can learn from data without having to be explicitly programmed. To predict stroke and its effects, a number of physiological and clinical indicators have been used. A cyber-physical stroke rehabilitation system (CP-SRS) as well as the modified Rankin Scale (mRS90) and National Institutes of Health Stroke Scale (NIHSS24) have both been predicted using ANN models. The results of this study indicate that neural networks might create a new and efficient way to categorize stroke patients’ risk.