Paralysis and Stroke Prediction System Using Machine Learning

Paralysis and stroke are debilitating medical conditions that can have life-altering consequences if not detected and treated early. Predictive models should be developed for early prediction of these conditions so that patients could take preventative measures. In this study, we propose a Paralysis and Stroke Prediction System (PSPS) which uses the Random Forest algorithm, which is one of the powerful machine learning techniques which is known to be the most accurate and versatile algorithm. The PSPS leverages a diverse set of patient's information, including demographic data like age, gender, medical chronicle, habits and practices, and medical examinations to create a robust prediction model. As said earlier, the Random Forest algorithm, which is one of the most versatile algorithms known to handle complex and large datasets, is employed to build an ensemble of decision trees that collectively provide accurate predictions. Ultimately, the PSPS has the potential to significantly improve patient care and reduce the burden of paralysis and stroke on healthcare systems.