HealthMate

HealthMate – Smarter wellness with AI and ML.

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HealthMate - AI & ML-Based Healthcare Web App

Web App Demo: [https://healthmate-pkumar.streamlit.app/]

Table of Contents 📚

  1. Project Overview
  2. Features
  3. Project Setup
  4. Datasets Used
  5. Tools & Technologies Implemented
  6. Code Structure
  7. Execution Instructions
  8. Future Scope
  9. Contributing

Project Overview 🏥

HealthMate is an AI & ML-based healthcare web application designed to provide:

  • Personalized Health Plans (Diet, Fitness, Hydration, Nutrition)
  • Doctor & Hospital Assistance (Find & consult with doctors and hospitals)
  • Government Health Scheme Eligibility Checker
  • Women’s Health Companion (Pregnancy Tracker, Menstrual Health Tracker)
  • Disease Predictions using advanced AI models The platform is accessible via a web-based interface, making healthcare services more accessible and efficient. 🌍

Features ✨

  1. Health & Wellness Plans 💪

    • Diet Plan: AI-driven diet recommendations. 🍎
    • Fitness Plan: Personalized workout plans. 🏋️‍♂️
    • Hydration Plan: Water intake tracking. 💧
    • Nutrition Plan: Balanced meal suggestions. 🥗

  2. Medical Assistance 🩺

    • Find doctors & hospitals in your city 🏥
    • Book consultations and appointments 📅
    • Doctor profiles, ratings, and specialties ⭐

  3. Government Scheme Eligibility Checker 🏛️

    • OCR Support: Scans ration/ID cards for quick user verification. 🪪
    • Real-Time Eligibility Check: Verifies eligibility for PM-JAY and other government health schemes. ✅

  4. Women’s Health Companion 👩‍⚕️

    • Pregnancy Stage Predictor: Estimates pregnancy stage. 🤰
    • Menstrual Cycle Tracker: Monitors cycles and fertility. 📆
    • Maternal Nutrition Guide: Suggests stage-specific diets. 🍼

  5. Disease Prediction Models 💡

    • Diabetes Prediction (Medical report analysis) 🍬
    • Heart Attack Prediction (Medical history & lifestyle analysis) ❤️
    • Lung Cancer Prediction (AI-based image recognition) 🫁
    • Asthma Prediction (Breath sound analysis using Deep Learning) 🌬️
    • Heartbeat Irregularities Prediction (Heart sound analysis) ❤️‍🩹

Project Setup ⚙️

  1. Prerequisites 🧑‍💻

    • Ensure you have the following installed:
      • Python 3.8+ 🐍
      • pip 📦
      • Virtual Environment (optional) 🌱

  2. Clone the Repository 🔁 git clone https://github.com/yourusername/HealthMate.git cd HealthMate

  3. Create Virtual Environment (Optional but Recommended) 🛠️ python -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts ctivate # On Windows

  4. Install Dependencies ⚡ pip install -r requirements.txt

Datasets Used 📊

  • Medical Reports Dataset: Used for diabetes, heart attack, and lung cancer predictions. 💉
  • Audio Dataset: Used for breath sound and heart sound analysis. 🎧
  • Health & Nutrition Data: Used for generating diet and fitness plans. 🍽️
  • Doctor & Hospital Data: Integrated with Google Maps API for location-based search. 🌍
  • Women’s Health Data: Used for pregnancy and menstrual health tracking. 🤰📆
  • Government Schemes Data: Provides information on health schemes and eligibility. 🏛️

Tools & Technologies Implemented 🛠️

Backend 🔌

  • Python 🐍
  • Flask 🖥️
  • Firebase (Database for user data & medical records) 🔒
  • PostgreSQL (User data storage) 🗃️

Machine Learning & AI Models 🤖

  • ML Algorithms: Random Forest, XGBoost, SVM 🌳
  • Deep Learning: CNN, LSTM for sound-based analysis 🧠
  • Data Processing: Pandas, NumPy, Scikit-Learn, TensorFlow, Keras 📊

Frontend & Deployment 🌐

  • Streamlit (Web App UI/UX) 🌟
  • HTML/CSS (For additional UI customization) 🎨
  • APIs: Google Maps API, OpenAI API 📍
  • Deployment Platforms: Streamlit Cloud, GCP ☁️

Code Structure 🗂️

HealthMate/ │── models/ # Machine Learning & Deep Learning models 🧑‍💻 │── static/ # Static files (CSS, Images, JS) 🖼️ │── templates/ # HTML templates for Streamlit 📝 │── datasets/ # Contains training datasets 📂 │── main.py # Main application entry point 💻 │── requirements.txt # Required dependencies 📦 │── README.md # Project documentation 📘

Execution Instructions 🏃‍♂️

  1. Run the Web App Locally 💻 streamlit run main.py

  2. Run on Google Cloud / Streamlit Cloud ☁️ Deploy the app on Streamlit Cloud following the official documentation. Configure Google Cloud App Engine for large-scale deployment.

Future Scope 🔮

  • Integration with Wearable Devices (Smartwatches for real-time health tracking) ⌚
  • Blockchain-based Medical Data Security (For secure patient records) 🔐
  • AI Voice Assistant for Health Queries 🎙️
  • More Disease Predictions (Kidney Disease, Obesity, Mental Health Analysis) 🧠

Contributing 🤝

  1. Fork the repository 🍴
  2. Create a new branch (git checkout -b feature-branch) 🌱
  3. Commit changes (git commit -m 'Added new feature') 📝
  4. Push the branch (git push origin feature-branch) 🚀
  5. Submit a Pull Request 🔄

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