🩺 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

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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.

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🛠️ 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

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##  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

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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.

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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).

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⚡ 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

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🧑‍🤝‍🧑 Contributors

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

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📜 License

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