Stroke Risk predictor

Problem Statement:

About 15 million people suffer from a stroke per year. It generally occurs due to Age, poor lifestyle and improper habits.

Proposed Solution:

A possible solution would be to create a Machine Learning model which would predict whether the person is a risk of future strokes, given their clinical features.

Data Description:

Dataset available in kaggle: Link

Sources: Source is kept confidential.

Attribute Information:

1. id: unique identifier
2. gender: "Male", "Female" or "Other"
3. age: age of the patient
4. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
5. heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease
6. ever_married: "No" or "Yes"
7. work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"
8. Residence_type: "Rural" or "Urban"
9. avg_glucose_level: average glucose level in blood
10. bmi: body mass index
11. smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"*
12. stroke: 1 if the patient had a stroke or 0 if not *Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Project Tree Structure

 .
├── Analysis
    ├── EDA.ipynb
    ├── Model_selection.ipynb
├── Datasets
     ├── healthcare-dataset-stroke-data.csv
     ├── preprocessed.csv
├── __pycache__
     ├── app.cpython-38.pyc
├── models
     ├── rf.sav
     ├── scaler.pkl
├── src
     ├── model_creation.py
     ├── preprocessing.py
├── templates
     ├── home.html
     ├── nostroke.html
     ├── stroke.html
├── Procfile
├── README.md
├── app.py
└── requirements.txt

Tools used:

• Programming language : Python
• IDE : Visual Studio Code
• Visualization : Matplotlib and Seaborn
• Deployment platform : Heroku
• Front end development : HTML/CSS
• Back end development : Flask
• Version control system : GitHub

Web App:

Web App Link: https://stroke-predict-app.herokuapp.com

In this web app, we just need to enter 10 clinical features about the person and the algorithm will predict if the person is at a risk of stroke.

Creator:

1. Hrishikesh Dutta