CardioSense is a cutting-edge mobile application designed to predict cardiovascular diseases by analyzing ECG signals. The app uses advanced machine learning algorithms to detect potential heart conditions in real-time, offering users immediate and actionable insights. With its intuitive interface, CardioSense empowers individuals to monitor their heart health proactively, supporting early detection and timely medical intervention.
Key Features:
- Real-Time ECG Analysis: Leverages advanced machine learning models to analyze ECG signals and identify irregularities indicative of heart conditions.
- User-Friendly Interface: Simplified and intuitive design ensures ease of use for all users, enabling efficient heart health monitoring.
- Actionable Insights: Provides immediate feedback and recommendations based on analysis, guiding users to seek medical attention when needed.
- Proactive Health Monitoring: Encourages regular monitoring to support early detection and prevention of cardiovascular issues.
Project Challenges:
- Developing efficient algorithms to process ECG signals in real-time while maintaining accuracy.
- Ensuring the app adheres to medical-grade standards for data analysis and recommendations.
- Creating an accessible and user-friendly interface for individuals with varying technical expertise.
Technologies Used:
- Mobile Development: Flutter/Dart or React Native for cross-platform compatibility.
Backend: Python (for machine learning model integration and data processing).
Machine Learning: TensorFlow
- PyTorch (for ECG signal analysis and prediction models).
Data Security: Compliance with healthcare standards (e.g. HIPAA) to ensure user data privacy and security.
