STROKE PREDICTION

Machine Learning Application

• Users are given a number of questions by the application, and based on their responses, it can determine whether they are prone to stroke.
• It utilizes a dataset from Kaggle that predicts a patient's likelihood of having a stroke based on inputs like gender, age, and other conditions like hypertension, BMI, glucose levels etc..
• For those users who are unsure of their BMI ranges, it offers a BMI checker.
• Logistic regression, a machine learning algorithm used for binary classification, is used along with the Python framework Flask to connect with the backend.

Prediction model

• LOGISTIC REGERESSION

HOME PAGE

BMI CHECKER

FORM

RESULT