🏥 AI-SEVA: Intelligent Healthcare Platform

A full-stack AI-driven healthcare platform that allows users to enter symptoms in natural language and receive real-time disease predictions, nearby doctor suggestions, and multilingual health news.

The platform is designed to assist rural communities in finding relevant healthcare information quickly.

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🚀 Features

🩺 AI Symptom Checker

• Uses Natural Language Processing (NLP) to analyze user-entered symptoms.
• Built with Scikit-learn, CountVectorizer, and Multinomial Naive Bayes.
• Predicts potential diseases and identifies possible medical emergencies.

🗺️ Find Nearest Doctors

• Interactive maps built using Leaflet.js.
• Displays nearby doctors and healthcare facilities based on the user's location and condition.

🌐 Multilingual Health News

• Displays latest healthcare news.
• Supports multiple languages (English & Hindi) for wider accessibility.

⚡ Modern Tech Stack

Frontend

• Next.js 15
• React 19
• TypeScript
• Tailwind CSS
• Shadcn UI

Backend

• Flask
• Python

Machine Learning

• Scikit-learn
• CountVectorizer
• Multinomial Naive Bayes

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🛠 Installation & Setup

Prerequisites

Make sure you have installed:

• Node.js
• Python 3

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Frontend Setup (Next.js)

Clone the repository:

git clone https://github.com/yourusername/ai-seva.git
cd ai-seva/GramAarogya-master

Install dependencies:

npm install

Run the development server:

npm run dev

The frontend will run at:

http://localhost:3000

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Backend Setup (Flask API)

Open a new terminal and navigate to the project directory:

cd ai-seva/GramAarogya-master

Create and activate a virtual environment (recommended):

python -m venv venv

Activate the environment

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

Install Python dependencies:

pip install -r requirements.txt

Run the backend server:

python backend.py

The backend API will run at:

http://localhost:5000

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🧠 Machine Learning Pipeline

The system uses a custom NLP pipeline to classify symptoms and predict diseases.

simulate_ml.py

Contains the training logic using:

• CountVectorizer
• Multinomial Naive Bayes

train_model.py

• Trains the ML model on symptom datasets
• Exports the trained model as:

model.pkl

run_metrics.py

Evaluates model performance by generating:

• Accuracy score
• F1 score
• Confusion matrix

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🤝 Contribution

Contributions are welcome! Feel free to fork the repository and submit pull requests.

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