stroke prediction Using Machine Learning
Author(s):
Ashwini RL, Dr. Shanthi DL, B.D.N.S Thanmai, Bhavyatha M, T. Ravinchandrakanth
Keywords:
Machine Learning, Classifier, Accuracy, Brain Stroke, Prediction.
Abstract
Strokes are serious medical emergencies that require immediate attention. They can lead to significant impairments in various bodily functions and cognitive abilities. Understanding the importance of maintaining healthy blood flow to the brain is crucial for preventing strokes and minimizing their impact. It's also essential to recognize the signs of a stroke, such as sudden weakness or numbness in the face, arm, or leg, especially on one side of the body, sudden confusion, trouble speaking or understanding speech, sudden trouble seeing in one or both eyes, sudden trouble walking, dizziness, loss of balance, or coordination, and sudden severe headache with no known cause. Seeking prompt medical care if you or someone you know experiences these symptoms can make a significant difference in the outcome. This project centers on creating a web application for Stroke Prediction utilizing Machine Learning. A stroke, a critical medical event, happens when blood flow to the brain is interrupted, resulting in potentially fatal outcomes. Numerous elements influence the risk of stroke, such as high blood pressure, diabetes, and lifestyle preferences. To refine stroke prediction, we utilize machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), Extratree Classifier, Gaussian Naive Bayes, Decision Tree, and Random Forest Classifier. These algorithms scrutinize user input data, taking into account a variety of risk factors. The experimental result shows that the Extratree Classifier achieves highest accuracy of 84%. We have developed the Web Application using flask framework to demonstrate the brain stroke prediction using Machine Learning.
Article Details
Unique Paper ID: 164534

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1840 - 1846
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