HealthEye 🧠💉

"Predict Early, Live Fully."
An AI-Based Disease Prediction System for personalized, accessible, and secure health risk assessment.

---

🚀 Project Overview

HealthEye is an AI-powered platform designed for early disease prediction, offering personalized healthcare insights based on medical history and lifestyle factors. Our goal is to empower users and healthcare providers with timely diagnostics, ultimately enhancing decision-making and promoting preventive care.

This project was developed as part of Codictive 3.0 – a state-level hackathon under the theme Health and Wellness.

---

🧠 Features

• AI-driven health risk analysis
• Early detection of diseases
• Lifestyle and medical history-based predictions
• Chatbot assistance for user interaction
• Scalable for personal and clinical use
• Strong focus on privacy and security

---

🛠 Tech Stack

Category	Tools
Data Collection	CSV, Excel, SQL
Preprocessing	NumPy, Pandas (imputation, outlier removal)
Feature Engineering	Scikit-learn, XGBoost (rolling averages, feature selection)
Model Training	Scikit-learn, XGBoost
Validation Metrics	Recall Score, Accuracy Score
Deployment	Streamlit, Binary files
Infrastructure	Local / Cloud, Git

---

Project Structure

Disease/
├── app.py                # Main application file
├── requirement.txt       # List of dependencies
├── .streamlit/           # Streamlit configuration files
├── Dataset_models/       # Dataset and model files
├── Models/               # Trained machine learning models
├── pages/                # Additional pages for the app
├── Image/                # Images and assets for the UI
├── utils/                # Utility functions for the app
└── venv/                 # Virtual environment folder

---

Requirements

1. Python 3.x
2. Streamlit
3. scikit-learn
4. pandas
5. numpy
6. matplotlib

---

Installation & Setup

1. Clone the Repository

git clone <repository-url>
cd Disease

2. Create a Virtual Environment

python -m venv venv

3. Activate the Virtual Environment

For Windows:

.\venv\Scripts\activate

For Mac/Linux:

source venv/bin/activate

4. Install Dependencies

pip install -r requirement.txt

5. Run the App

streamlit run app.py

---

📈 Use Cases

• Early disease detection
• Personalized risk assessment
• Preventive healthcare suggestions
• Hospital resource optimization
• Clinical decision support
• Population health monitoring

---

💡 Business Model

• Freemium Model – Basic predictions for free, advanced insights under premium
• Subscriptions – Monthly/yearly plans for clinics and hospitals
• Doctor Consultations – Commission via platform bookings
• B2B Licensing – Sell APIs/tools to clinics & insurance
• Health Ads & Partnerships – Revenue from brands, labs, wellness orgs
• Data Insights – Anonymized research data (with user consent)

---

🔮 Future Scope

• Integration with smartwatches & wearables
• Multilingual & mobile app accessibility
• Telemedicine & doctor-booking support
• Public health partnerships (Govt./NGOs)
• Personalized dashboards and reports

---

📍 Team - HealthTech Innovators

Name	Role	Branch	Year
Yash Kumar Rahadve	Team Leader	CSE	2nd
Raunak Kesharwani	Member	CSE	2nd
Shruti Mehra	Member	CSE	2nd
Muskan Kawadkar	Member	CSE	2nd
Shiv Kumar Rai	Member	CSE	2nd
Ajay Wadekar	Mentor	Expert in Image Processing	12+ yrs

---

🏫 Institute

Bansal Institute of Research Technology and Science, Bhopal