A Stroke Complication Neural Network Model to Predict the Severity of Brain Stroke Using Family History

These days, due to technological advancements, the lifestyle of modern humans has changed from being active to sedentary. As computational power is rapidly increasing day by day, we can use concepts like machine learning and neural networks to estimate and monitor the health of people by just using stroke parameters along with family history. The findings in the body of literature are already in existence, which show that brain strokes can be classified without considering the significance of family history. In this work, ischemic, intracerebral, and subarachnoid hemorrhagic brain strokes are the main emphasis. A Stroke Complication Neural Network (SCNN) is proposed. This model works by taking the stroke classification results from machine learning models. Using this stroke complication graph, family history, and a multi-layered perceptron (MLP), an attempt is made to determine the severity of the stroke. The proposed model has obtained decent outcomes with an accuracy of 94.32%.