CardioCare ❤️ AI-Powered Website

⚠️ Important Warning

This project focuses on integrating AI into the web to provide an initial overview of heart disease risks.

The AI predictions provided are not for medical reference!

• CardioCare is not a medical diagnostic tool.
• The predictions shown are based solely on the AI model and the data entered by the user.
• Please consult your doctor or a healthcare professional for accurate diagnosis and treatment.

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Project Goals 🎯

To provide health education and an initial overview of heart disease risks by integrating AI technology into an easy-to-use web platform.

About the Project 📋

CardioCare is an interactive landing page that helps users:

• Learn about heart disease education through informative content.
• Check their heart health risk using AI trained with Scikit-Learn.

This project combines AI technology and modern web to create an interactive and user-friendly experience.

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Key Features ✨

1. Heart Disease Education
   Complete and easy-to-understand information about heart disease risks.
2. Heart Disease Risk Check
   • Users fill out a simple form (age, blood pressure, cholesterol, etc.).
   • The Flask backend runs the AI model to predict the health risk.
   • The result, showing the initial risk, is displayed on the ReactJS frontend.

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Technologies Used 🛠️

Frontend

1. ReactJS: Building the interactive user interface.
2. Axios: Connecting the React frontend with the Flask backend.
3. TailwindCSS: Modern and responsive styling.
4. Framer Motion: Make any design animated.

Backend

1. Flask: To create the API that receives data from the user and processes the AI model.
2. Scikit-Learn: Training and running the heart disease risk prediction model.
3. Pickle: Storing the trained AI model for future use.

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API Documentation 📄

Endpoint: /predict

Method: POST
Description:
This endpoint is used to send user data and receive a prediction regarding the heart disease risk based on the provided input.

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Required Data (Request Body)

The following data must be sent in JSON format:

Parameter	Data Type	Description
age	float	User's age (e.g., 45.0)
sex	int	Gender (1 = Male, 0 = Female)
cp	int	Chest pain type (using category numbers)
trestbps	float	Resting blood pressure (e.g., 130.0)
chol	float	Cholesterol level (e.g., 250.0)
fbs	int	Fasting blood sugar (1 = >120 mg/dL, 0 = ≤120 mg/dL)
restecg	int	Resting electrocardiographic results (category number)
thalach	float	Maximum heart rate during physical activity
exang	int	Exercise-induced chest pain (1 = Yes, 0 = No)
oldpeak	float	ST depression during exercise test (e.g., 1.2)
slope	int	Slope of the peak exercise ST segment (category number)
ca	int	Number of major vessels colored by fluoroscopy
thal	int	History of thalassemia (category number)

Once the data is submitted, the server will return a response in JSON format containing the prediction result and suggestions for further steps.

Response Structure:

{
  "prediction": "Heart Disease Detected",
  "suggestion": "We recommend consulting a doctor for further evaluation."
}