Skip to content

README.md

Latest commit

 

History

History
117 lines (82 loc) · 3.67 KB

README.md

File metadata and controls

117 lines (82 loc) · 3.67 KB
Raw

🩺 Symptom-Based Multi-Cancer Risk Detection Tool

This project is a web-based application that helps users assess their risk of developing Breast Cancer, Lung Cancer, and Liver Cancer based on self-reported symptoms. The tool is designed to raise awareness, provide preliminary risk insights, and encourage users to seek professional medical consultation.

Disclaimer: This tool is not a medical diagnosis system. It is only for educational and awareness purposes. Always consult a qualified doctor for medical advice.

Live Demo

You can explore the live web app here: Symptom-Based Cancer Detection Tool

Features

  • Symptom-Based Detection: Users can enter their symptoms to get an estimated cancer risk score.
  • Cancers Covered:
  • Breast Cancer
  • Lung Cancer
  • Liver Cancer
  • Risk Prediction: Provides a simple low/medium/high risk assessment.
  • Responsive Web Interface built with HTML, Tailwind CSS, and JavaScript.
  • Blog Section: Awareness content for breast cancer and other cancers.
  • FAQs Section: Educates users about common cancer-related questions.
  • Contact & Newsletter: Allows users to get in touch and subscribe for updates.

🛠️ Tech Stack

  • Frontend: HTML, Tailwind CSS, JavaScript
  • Backend (Optional): Python (Flask/Django) or Node.js for risk prediction models
  • ML Models: Logistic Regression / LightGBM / Random Forest (trained on symptom datasets)
  • Deployment: Vercel / Firebase / Netlify
##  Project Structure  

symptom-cancer-detection/
│── index.html             # Homepage (UI)
│── blog.html              # Breast cancer awareness blog
│── faq.html               # FAQ section
│── contact.html           # Contact form
│── /static
│    ├── css/              # Tailwind CSS files
│    ├── js/               # Custom JavaScript for prediction logic
│    └── images/           # Project images & icons
│── /models
│    ├── breast_cancer.pkl # Trained ML model (Breast Cancer)
│    ├── lung_cancer.pkl   # Trained ML model (Lung Cancer)
│    └── liver_cancer.pkl  # Trained ML model (Liver Cancer)
│── app.py / server.js     # Backend (Flask/Node.js) to serve ML models
│── README.md              # Project documentation

How It Works

  1. User selects symptoms from a checklist or form.
  2. The system processes symptoms using trained ML models.
  3. A risk score is calculated (Low / Medium / High).
  4. Results are shown with awareness tips and a doctor consultation recommendation.

Machine Learning Approach

  • Data Preprocessing: Cleaning and encoding symptom datasets.
  • Model Training: Logistic Regression, Random Forest, LightGBM were tested.
  • Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.
  • Best Model: LightGBM (highest accuracy & efficiency).

⚡ Installation & Setup

1 Clone Repository

git clone https://github.com/your-username/symptom-cancer-detection.git
cd symptom-cancer-detection

2 Install Dependencies

If using Django (Python):

pip install -r requirements.txt

3. venv create

python3 -m venv env_name

active this venv

4. Run the project

python3 manage.py runserver

🧑‍🤝‍🧑 Contributors

  • 👨‍💻 [Md. Atiar Rahman and others] – Developer & Researcher
  • 🩺 Medical Advisors (Optional if collaborated)

📜 License

This project is licensed under the MIT License – free to use and modify with attribution.