Stroke Prediction System using Linear Regression

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A stroke is defined as an acute neurological disorder of the blood vessels in the brain that occurs when the blood supply to an area of the brain stops and the brain cells are deprived of the necessary oxygen. According to the World Stroke Organization, 13 million people get a stroke each year, and approximately 5.5 million people will die as a result. It is the leading cause of death and disability worldwide, and that is why its imprint is serious in all aspects of life. Stroke not only affects the patient but also affects the patient’s social environment, family and workplace. In addition, contrary to popular belief, it can happen to anyone, at any age, regardless of gender or physical condition.

To help save a life who might have a probability of stroke, we have designed a Stroke Prediction System using Linear Regression. The objective of implementing the system on a web platform is to reach as many individuals as possible. The development of this ML model could aid in the early detection of stroke and the subsequent mitigation of its severe consequences.

The system comprises 1 module namely User.
The user would require to register first to access the system. After registering successfully, the user can log in using their credentials. In order for the system to predict if there is a risk of stroke, the user would require to enter certain inputs. If there is a risk of stroke, the system will redirect to a page where the user can view the nearby hospitals and the stroke details along with its causes, symptoms and treatment.

The technologies used to develop this system involve HTML, CSS and JavaScript in the front end and Python in the backend. The database used is MySQL and the framework used is Django. The dataset is used from Kaggle. The algorithm used to design the working of this system is Logistic Regression. It is used to characterize the data and illustrate the association between one dependent binary variable and one or more conditional, ordinal, period, or ratio-level independent variables.


  • The system can be used to accurately predict stroke risk.
  • The user will be able to find hospitals nearby using the system.
  • The system will show the type of stroke along with its symptoms and treatment.
  • The system is easy to use and efficient.