🧠 NeuroRelief AI

AI-Powered Personalized Pain Assessment and Relief Recommendation System

📌 Overview

NeuroRelief AI is an intelligent healthcare application designed to assist users in assessing pain levels and receiving personalized relief recommendations using Artificial Intelligence. The platform analyzes user symptoms, health parameters, and lifestyle factors to predict pain severity and provide suitable guidance.

The goal of NeuroRelief AI is to improve accessibility to preliminary healthcare support while promoting informed decision-making through AI-driven insights.

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🚀 Features

• 🤖 AI-based pain severity prediction
• 📊 Personalized health assessment
• 💊 Relief recommendations based on symptoms
• 📈 Interactive dashboard
• 👤 Secure user authentication
• 📱 Mobile-friendly user interface
• 🔍 Health history tracking
• 🧠 Machine Learning prediction model

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🏗️ System Architecture

User
   │
   ▼
Frontend (React / React Native)
   │
   ▼
FastAPI Backend
   │
   ▼
Machine Learning Model
   │
   ▼
Database

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🛠️ Tech Stack

Frontend

• React Native / React
• TypeScript
• Expo

Backend

• FastAPI
• Python

Machine Learning

• Scikit-learn
• Pandas
• NumPy

Database

• Supabase / SQLite / PostgreSQL

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📂 Project Structure

NeuroRelief-AI/
│
├── frontend/
├── backend/
├── model/
├── dataset/
├── assets/
├── documentation/
├── requirements.txt
└── README.md

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⚙️ Installation

Clone Repository

git clone https://github.com/yourusername/NeuroRelief-AI.git

Move into Project

cd NeuroRelief-AI

Install Backend Dependencies

pip install -r requirements.txt

Start Backend

uvicorn main:app --reload

Start Frontend

npm install
npm start

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📊 AI Workflow

1. User enters symptoms and health information.
2. Data is preprocessed.
3. Machine Learning model predicts pain severity.
4. Personalized recommendations are generated.
5. Results are displayed through an interactive dashboard.

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🎯 Objectives

• Improve early pain assessment.
• Assist users with AI-driven recommendations.
• Enhance healthcare accessibility.
• Promote preventive healthcare practices.

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🔒 Security Features

• Secure authentication
• Encrypted user data
• Protected API endpoints
• Privacy-focused architecture

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📈 Future Scope

• Wearable device integration
• Real-time health monitoring
• Doctor consultation module
• Multi-language support
• Cloud deployment
• Advanced Deep Learning models

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👨‍💻 Developed By

Saket Raj

Computer Science Engineering
SJBIT, Bengaluru

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📜 License

This project is developed for educational and research purposes.