Lung Cancer Prediction API 🚀

📋 Project Description

This project is a Lung Cancer Prediction API built using Flask and a machine learning model. It predicts the risk of lung cancer based on various input features such as age, smoking habits, chronic lung disease history, and other medical attributes. The aim is to provide a tool for early detection of lung cancer to help with timely intervention.

🚀 Features

• Prediction API: Provides lung cancer risk prediction based on input parameters.
• ML Model: A trained machine learning model that uses patient data to predict lung cancer risk.
• Flask Backend: A lightweight backend framework serving the prediction API.
• Interactive Frontend: A user-friendly interface built with React and Tailwind CSS for making predictions.
• Responsive Design: Ensures a smooth experience on both desktop and mobile devices.

🛠 Tech Stack

• Backend: Flask, Python
• Frontend: React, Tailwind CSS, Motion
• Machine Learning: Scikit-learn, Pandas, NumPy
• Deployment: Localhost for development

📂 Project Structure

├── backend
│   ├── app.py
│   ├── model.pkl
│   ├── requirements.txt
│   ├── utils.py
├── frontend
│   ├── src
│   │   ├── components
│   │   │   ├── PredictionForm.js
│   │   │   └── ResultCard.js
│   │   ├── App.js
│   │   └── index.js
│   └── public
│       └── index.html
├── README.md
└── .gitignore

⚙️ Installation

Prerequisites

• Python 3.x
• Node.js
• npm / yarn
• Git

1. Clone the Repository

git clone https://github.com/RAGAV-24/lung-cancer-prediction.git
cd lung-cancer-prediction

2. Backend Setup

Navigate to the backend folder and install the required Python packages.

cd backend
pip install -r requirements.txt

3. Run the Flask Backend

python app.py

The backend server will start at:

• Local: http://127.0.0.1:5000
• Network: http://192.168.x.x:5000 (Replace with your network IP if needed)

4. Frontend Setup

Navigate to the frontend folder and install the dependencies.

cd ../frontend
npm install

5. Run the Frontend

npm run dev

Open your browser and navigate to:

http://localhost:5173

🌐 API Endpoints

POST /predict

Predict the risk of lung cancer based on patient data.

Request

POST /predict
Content-Type: application/json

{
  "age": 55,
  "smoking": 1,
  "yellow_fingers": 0,
  "anxiety": 1,
  "peer_pressure": 1,
  "chronic_disease": 1,
  "fatigue": 0,
  "allergy": 0,
  "wheezing": 1,
  "alcohol_consuming": 0,
  "coughing": 1,
  "shortness_of_breath": 1,
  "swallowing_difficulty": 1,
  "chest_pain": 1
}

Response

{
  "prediction": "High Risk",
  "guidance": "Immediate medical consultation is advised."
}

🐞 Troubleshooting

• CORS Error: Ensure the CORS is configured in app.py:

  from flask_cors import CORS
  CORS(app)

• 400 Bad Request: Check if the input payload format is correct when sending requests to the API.

📝 Requirements

Create a requirements.txt in the backend folder with the following content:

flask
flask-cors
scikit-learn
pandas
numpy

Install the requirements:

pip install -r requirements.txt

📈 Future Enhancements

• Deployment: Plan to deploy on cloud platforms like AWS, Azure, or Heroku.
• Enhanced Model: Improve the model with more features and a larger dataset.
• Additional Features: Add user authentication, data visualization, and downloadable reports.

🤝 Contribution

Feel free to contribute to this project by creating issues or submitting pull requests.