Stroke is one of the leading causes of death and disability globally. Early detection and risk prediction can significantly improve outcomes by enabling timely medical intervention. This project aims to build a simple — yet effective — open-source solution that:
Uses publicly available healthcare datasets to analyze risk factors.
Trains a machine-learning model to predict an individual’s likelihood of stroke based on risk parameters (age, health metrics, medical history, etc.).
Provides a minimal web-based interface so users or clinicians can input parameters and get a stroke-risk prediction.
Enables further research, experimentation or integration with more sophisticated medical-grade systems.
This repository includes data preprocessing notebooks, model training scripts, a trained model, and a basic web application to run predictions.
✅ Key Features
Data-driven: Uses a structured healthcare dataset (CSV) containing features relevant to stroke risk.
Model training pipeline: Scripts to preprocess data (.ipynb files), train a model (train_model.py), and save it (model.pickle).
Web application: A simple app (e.g. app.py) demonstrating how to serve predictions — useful for demos or light use.
Full project transparency: All code, data processing steps, and notebooks are included, enabling reproducibility and extension.
Easy to extend: As open source — you can retrain the model with bigger/more diverse datasets, refactor for production, or add user-management, logging, alerts, etc.
📁 Repository Structure / — root directory
├─ healthcare-dataset-stroke-data.csv # original dataset
├─ brainstoke-data.ipynb / brain stroke-image.ipynb # data analysis & EDA notebooks
├─ train_model.py # script to train ML model
├─ model.pickle # trained ML model file
├─ app.py # web application to accept inputs & return prediction
├─ database.py / database.db # optional DB for storing user/prediction history
├─ users.json / users.db # optional user data / credentials
├─ main.py # (if used) wrapper / orchestration script
├─ README.md # this file: project overview, instructions, etc.
🛠️ Requirements & Dependencies
Python 3.x
Common data-science & ML libraries: e.g. pandas, scikit-learn, numpy (specify versions)
For web interface: Flask (or whichever micro-framework you choose) + relevant dependencies
(Optional) Database dependencies if using database.py (e.g. sqlite3, or other)
Provide a requirements.txt or environment.yml file listing all dependencies for easy setup.
🚀 Getting Started
Clone the repository:
git clone https://github.com/NKPoorvika/Brainstroke-Detection-Predection.git
Create and activate a Python virtual environment.
Install dependencies:
pip install -r requirements.txt
(Optional) Run notebooks to explore dataset / preprocess data.
Train the model (or use the provided model.pickle).
python train_model.py
Start the web app:
python app.py
Open the app in browser (e.g. http://localhost:5000) and input health parameters to get a stroke risk prediction.
📚 Use Cases
Academic / educational: Learn about risk-factor analysis, medical data modelling, ML pipeline.
Prototype for medical-tech: Extend for real hospital/clinic-grade system with better dataset & security.
Public awareness: Build a tool for individuals to check their stroke risk (with disclaimers).
Research: Extend to deep-learning, more features (imaging data), integrate with other medical data for improved prediction accuracy.
Not a medical device: This is a proof-of-concept / research-oriented tool. Predictions are statistical estimates, not medical diagnoses.
Model trained on one (potentially limited) dataset — may not generalize across demographics/geographies.
Input data quality matters: incorrect or incomplete data may lead to inaccurate predictions.
If you intend to deploy publicly or use for real patients — consult a medical professional, implement data privacy, validation, and regulatory compliance.
📄 License & Contribution
Specify a license (e.g. MIT, Apache-2.0) in a LICENSE file.
Add CONTRIBUTING.md for guidelines on how others can contribute (e.g. dataset updates, model improvements, UI enhancements).
Encourage users to fork, star, report issues, and submit pull requests for improvements.
🧩 Future Work / Roadmap
Use larger and more diverse datasets (public / open medical data) to retrain and improve model robustness.
Add support for more features (e.g. medical imaging, time-series data, user history).
Improve UI/UX of web app (interactive form, better error handling, deployment via Docker / cloud).
Add tests, CI/CD, code quality checks.
Develop documentation for model evaluation, limitations, and data privacy best practices.