A Machine Learning-Based Web Application for Brain Stroke Risk Prediction
A Machine Learning-Based Web Application for Brain Stroke Risk Prediction
1 A UshaRani
Assistant Professor , ushaviit@gmail.com
1G Partha Srikar
1Abdul Irfan
1Duvvi K S S V Vynika
1Bhadidha Shandeepa
1Department of Computer Science & Engineering, Vignan's Institute of Information Technology, Visakhapatnam
Abstract
A major cause of lasting harm and loss of life across the globe is stroke. Spotting warning signs early helps lower chances when prevention steps are taken. What you get here is an online tool that guesses stroke likelihood using smart software trained on personal health details - things like how old someone is, blood pressure levels, weight patterns, and daily routines. Built into a website people can navigate easily, it connects smoothly to a processing core that delivers fast feedback. Several types of algorithm setups went into testing; out of them, XGBoost stood apart by correctly predicting outcomes 93% of the time. Finding shows how well the new method works in guessing stroke chances, helping people notice issues sooner. Health tracking becomes possible when users check their condition through the app with accurate monitoring and timely updates.
Keywords: Stroke Prediction, Machine Learning, Healthcare Analytics, Web Application, Preventive Healthcare.