# Preventive Healthcare System

This project is an **AI-powered Predictive Healthcare System** that helps identify individuals at risk of developing chronic diseases such as **diabetes, heart disease, or obesity**. The system provides personalized recommendations for preventive care based on lifestyle and medical data.

## Features

- AI-based health risk prediction for **diabetes**, **heart disease**, and **obesity**.

- User-friendly form for inputting health and lifestyle data.

- Displays risk probabilities and provides feedback on how to manage risks.

- Uses pre-trained machine learning models to assess the risk levels.

- Integrated with **Streamlit** for interactive web-based applications.

## Prerequisites

Ensure you have the following installed:

- Python 3.7+

- `streamlit`

- `pandas`

- `scikit-learn`

- `requests`

- `Pillow` (for image processing)

Install all the necessary dependencies using the following command:

```bash

pip install -r requirements.txt

```

## Installation & Setup

1. **Clone the repository**:

```bash

git clone https://github.com/aaarif796/AI-Powered-Preventive-Healthcare-System.git

cd AI-Powered-Preventive-Healthcare-System

```

2. **Download or Place Model Files**:

Make sure to have the pre-trained model files:

- `label_encoders.pkl`

- `lr_dt.pkl` (Logistic Regression model for Diabetes)

- `lr_ht.pkl` (Logistic Regression model for Heart Disease)

- `lr_ob.pkl` (Logistic Regression model for Obesity)

Place these files inside the `model` folder.

3. **Add Images**:

Place relevant images in the `images` folder for visual representation.

4. **CSS Styling**:

The application uses a custom CSS file for styling. Ensure you have the `style.css` file in the `style` folder.

## Usage

1. **Run the Application**:

Use Streamlit to launch the app with the following command:

```bash

streamlit run app.py

```

2. **Input Data**:

Fill out the form with your general health and lifestyle details (e.g., age, exercise habits, smoking history, etc.).

3. **Receive Feedback**:

The app will predict your risk level for **diabetes**, **heart disease**, and **obesity** based on the data you provide. It will also offer personalized advice based on the risk level.

## Folder Structure

```

├── images

│   ├── healthcare.webp

├── model

│   ├── label_encoders.pkl

│   ├── lr_dt.pkl

│   ├── lr_ht.pkl

│   ├── lr_ob.pkl

├── style

│   ├── style.css

├── app.py

├── README.md

├── requirements.txt

```

## Model Details

- **label_encoders.pkl**: Used to encode categorical data.

- **lr_dt.pkl**: Logistic Regression model for predicting the risk of diabetes.

- **lr_ht.pkl**: Logistic Regression model for predicting heart disease risk.

- **lr_ob.pkl**: Logistic Regression model for obesity risk.

## Acknowledgments

This application was developed as part of the **TechXcelerate 2024** challenge, focusing on developing a predictive healthcare system using machine learning and AI.