Early Detection of Brain Stroke using Machine Learning Techniques
V. Gopal Krishna, J. Sasi Kiran, D. P. Rao, G. Charles Babu, G. John Babu
Abstract
The brain is the most complex organ in the human body. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke). Early brain stroke prediction yields a higher amount that is profitable for the initiating time. Brain stroke is caused primarily by people’s lifestyle decisions, particularly in the current scenario by evolving elements such as high blood sugar, heart disease, obesity, diabetes, and hypertension. This research study has used various machine learning (ML) algorithms like K nearest neighbour, logistic regression, random forest (RF) classifier and SVC. This research work designs a model using one among the following algorithms with high accuracy to predict the stroke for newly given inputs.