Skip to content

This mobile health app helps cardiovascular patients monitor their health using machine learning. It tracks key metrics, predicts potential risks, and provides personalized insights and real-time guidance to support better health management.

Notifications You must be signed in to change notification settings

it21270338/Mobile-based-Health-Monitoring-System-with-Machine-learning-Insights-for-Cardiovascular-Patients

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 

Repository files navigation

Mobile-based-Health-Monitoring-System-with-Machine-learning-Insights-for-Cardiovascular-Patients

Overview of the Project

The MediSafe project is a mobile-based health monitoring app designed to help cardiovascular patients manage their health more effectively. It uses advanced machine learning to analyze health data, predict risks, and provide actionable recommendations. The system collects user inputs like lifestyle habits and health metrics, processes them securely, and delivers personalized insights in real-time. The main goal of MediSafe is to create a user-friendly tool that combines accuracy and accessibility, making it easier for users to monitor their health and prevent potential risks. The app also offers features like 3D pain logging, personalized health tips, and dynamic health scoring to keep users engaged while improving their overall health.

Key Features:

  • Health Risk Predictions The app uses advanced machine learning models to predict the likelihood of developing health conditions like diabetes and heart disease. By analyzing your real-time and past health data, it provides highly accurate and reliable insights. This helps you understand potential risks early and take preventive measures.

  • Personalized Health Tips MediSafe offers tailored health advice and alerts based on your daily activities, health habits, and risk factors. These tips adapt over time to become more effective, keeping you motivated to make healthier choices. Early warnings and reminders ensure you stay on top of your health goals without feeling overwhelmed.

  • 3D Pain Logging and Alerts The app includes an interactive 3D body map where you can log areas of pain easily. It detects critical pain patterns in real-time and sends emergency alerts if needed. You can also track pain trends over time, helping both you and your doctor understand recurring issues and their triggers.

  • Dynamic Daily Health Score MediSafe calculates a daily health score that reflects your overall wellness. This score updates in real-time based on your habits and activities, giving you instant feedback on how your choices impact your health. It acts as a motivating factor to help you stay consistent with your lifestyle changes.

  • Accurate Data Handling To ensure reliability, the app validates and corrects any errors in your health data in real-time. It uses smart algorithms to identify outliers or inconsistencies and provides secure storage with strong privacy protections to keep your data safe.

  • Real-Time Updates and Alerts You’ll receive instant notifications about any significant changes or risks in your health data. These real-time updates make it easier to act quickly and avoid potential complications, ensuring you stay informed and in control of your health.

  • User-Friendly Design MediSafe is designed to be simple and intuitive, making it easy for anyone to use, regardless of their technical skills. It also incorporates engaging features like reminders, gamified notifications, and progress tracking to keep you interested and active in maintaining your health.

architectural diagram

architectural diagram drawio (1)

Dependencies

The following tools, libraries, and frameworks are used to build the system:

  • Technologies:

React Native for cross-platform mobile app development.

Python for data processing and machine learning.

TensorFlow for building and training ML models.

Google Colab for collaborative model development and experimentation.

  • Data Requirements:

Secure collection and preprocessing of user health data.

Reliable integration of lifestyle metrics for predictive analysis.

  • Infrastructure:

Cloud computing resources for training dynamic ML models.

Real-time data handling protocols like MQTT for seamless transmission.

  • Collaboration:

Partnership with healthcare institutions to validate predictions and recommendations.

Coordination among team members to finalize UI design, model training, and system integration.

About

This mobile health app helps cardiovascular patients monitor their health using machine learning. It tracks key metrics, predicts potential risks, and provides personalized insights and real-time guidance to support better health management.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published