https://github.com/whatyuupratama/aodycardio

https://github.com/whatyuupratama/aodycardio

classification flask

Last synced: 9 months ago
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• Host: GitHub
• URL: https://github.com/whatyuupratama/aodycardio
• Owner: whatyuupratama
• Created: 2024-12-27T10:09:36.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-27T10:27:08.000Z (over 1 year ago)
• Last Synced: 2025-03-01T15:32:14.789Z (over 1 year ago)
• Topics: classification, flask
• Language: Python
• Homepage: https://cardiocare.up.railway.app/
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **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.**

---

## **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.

---

## **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.

---

## **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.

---

# 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.

---

### 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:

```json

{

  "prediction": "Heart Disease Detected",

  "suggestion": "We recommend consulting a doctor for further evaluation."

}