Brain Stroke Prediction Utilizing Convolutional Neural Networks

A brain stroke is a serious medical illness that can have life-threatening consequences and is caused by to interruption in blood flow to the brain. Early detection and timely treatment can reduce mortality rates and also enhance patient recovery. Machine learning techniques have evolved into highly effective tools in recent years for predicting the risk of a brain stroke based on many health indicators. With a focus on important risk factors like age, blood glucose levels, heart disease, smoking, and hypertension, this study investigates the creation of a predictive model for brain stroke using convolutional neural networks. The goal of the model is to accurately classify individuals who are at risk by using a dataset that contains key medical information. The suggested method can help medical practitioners by facilitating early detection and preventative actions that minimize the impact of brain strokes.