🩺 AI-Powered Healthcare Intelligence Network
Revolutionizing Healthcare with AI-Driven Predictions, Recommendations, and Insights, Medibot(RAG)
📌 About This Project
The AI-Powered Healthcare Intelligence Network is a cutting-edge platform that leverages Machine Learning (ML) and Natural Language Processing (NLP) to provide accurate disease predictions, personalized medical recommendations, and AI-assisted drug suggestions. The system aims to enhance early diagnosis, reduce medical errors, and offer intelligent healthcare solutions.
https://github.com/user-attachments/assets/360876dc-551a-498b-ab75-472137fed751
🚀 Features
💡 Disease Prediction & Medical Recommendation
This module uses Machine Learning to predict diseases based on symptoms and suggest the best medical recommendations.
- ✅ Predicts diseases based on symptoms provided by the user.
- ✅ Uses RandomForest Classifier for predictions.
- ✅ Provides recommended treatments and precautions.
- ✅ Provides medical descriptions, precautions, medication suggestions, and diet recommendations**.
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💊 AI-Powered Drug Recommendation
Our AI system uses NLP & Cosine Similarity to recommend alternative medicines based on drug properties.
- ✅ AI-powered alternative medicine finder.
- ✅Utilizes **NLP & cosine similarity** for **accurate drug matching**
- ✅ Matches medicines with similar ingredients.
- ✅ Ensures safer and more effective drug prescriptions.
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🪀 Heart Disease Risk Assessment
This module uses LightGBM & AI classifiers to assess heart disease risks based on patient history.
- ✅ Evaluates heart disease risk based on lifestyle and medical history.
- ✅ Uses machine learning models (LightGBM, EasyEnsemble) for predicting heart disease risk.
- ✅ Takes inputs like age, BMI, smoking habits, medical history, etc.
- ✅ Provides a **personalized heart risk score with AI-driven recommendations**
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🤖 Medibot - AI Health Assistant
Our LLM-powered chatbot answers medical queries and provides instant healthcare insights using Hugging Face LLM (Mistral-7B-Instruct).
- ✅ AI-powered medical chatbot based on Mistral-7B-Instruct.
- ✅ Retrieves medical information from a FAISS vector database.
- ✅ Retrieves reliable medical information using RAG (Retrieval Augmented Generation.
- ✅ Provides fast, relevant, and fact-based healthcare responses.
- ✅ Provides reliable AI-driven answers to health-related questions.
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📂 Folder Structure
📦 AI-Powered Healthcare Intelligence Network │── 📂 models/ # Trained ML models │── 📂 data/ # Medical datasets (CSV) │── 📂 vectorstore/db_faiss/ # FAISS vector database │── 📂 utils/ # Images, styles, and helper files │── 📂 pages/ # Individual module pages │── 📜 home.py # Main homepage (Streamlit UI) │── 📜 requirements.txt # Dependencies │── 📜 README.md # Project Documentation │── 📜 .gitignore # Ignored files │── 📜 styles.css # Custom CSS for UI
⚙️ Installation & Setup
1️⃣ Clone the Repository
git clone https://github.com/AbhaySingh71/AI-Powered-Healthcare-Intelligence-System.git cd AI-Powered-Healthcare-Intelligence-System
2️⃣ Set Up the Virtual Environment
python -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows
3️⃣ Install Dependencies
pip install -r requirements.txt
4️⃣ Set Up Environment Variables
Create a .env file and add:
HF_TOKEN=your_huggingface_api_token
Ensure it is added to GitHub Secrets when deploying.
5️⃣ Run the Application
streamlit run home.py
🚀 Deployment on Streamlit Cloud
1️⃣ Push code to GitHub
git add . git commit -m "Initial commit" git push origin main
2️⃣ Deploy on Streamlit
- Go to Streamlit Cloud → Deploy a new app.
- Set
HF_TOKENin Streamlit Secrets. - Click Deploy! 🎉
⚙️ Technologies Used
- Machine Learning: RandomForest, LightGBM, NLP, Cosine Similarity
- AI & NLP: Hugging Face Transformers, LangChain, FAISS
- Data Handling: Pandas, NumPy, Pickle
- Web Framework: Streamlit
- Visualization: Plotly, SHAP for feature importance
- Cloud Deployment: AWS, GCP
🔍 Why Use This App?
- 🏥 AI-Powered Healthcare Insights: Get data-driven medical predictions.
- ⚕️ Enhances Patient Care: Supports doctors and patients in making informed decisions.
- 💡 Real-Time Recommendations: Provides immediate AI-assisted insights.
- ⏳ Saves Time: Automates diagnosis and medical recommendations.
- 🔬 Empowers Medical Research: Helps in early disease detection and prevention.
Docker Deployment
This project is Docker-first. Docker ensures that the model can run in any environment without worrying about Python versions, dependencies, or system settings.
docker pull abhaysingh71/ai-powered-healthcare-system
docker run -p 8501:8501 abhaysingh71/ai-powered-healthcare-system
✅ Why Docker?
- Environment-independent deployments
- Fast setup and teardown
- Easy to host on cloud (AWS, GCP, Azure)
- Reproducibility for teams and CI/CD pipelines
🌐 Docker hub
📜 License
This project is licensed under the MIT License. Feel free to use, modify, and contribute!
📬 Contact Us
Have questions or need support? Reach out to us at: