FYP

CardioGraph Pro

CardioGraph Pro is a comprehensive system for early detection and management of heart disease. It integrates advanced machine learning, ECG signal analysis, demographic data, image processing, and user-friendly interfaces for accurate predictions and personalized guidance.

Table of Contents

• Abstract
• Project Motivation
• Features
• Technologies
• Installation and Setup
• Usage Instructions
• Architecture
• Data Collection and Preprocessing
• Model Development and Training
• Deployment
• Results and Evaluation
• Contributers
• More Details
• Let’s Connect

Abstract

CardioGraph Pro offers innovative solutions to address the challenges of timely heart disease diagnosis. It combines ECG signal analysis, demographic data integration, and image-based ECG report processing to enhance prediction accuracy. The project’s user-centric design, including a web interface, mobile app, and medical chatbot, prioritizes accessibility and promotes disease management for patients.

Project Motivation

CardioGraph Pro is driven by the need for early and reliable detection of heart disease, a leading cause of mortality worldwide. By integrating multiple data sources and leveraging machine learning, the project aims to:

• Improve Prediction Accuracy: Achieve superior results compared to traditional methods.
• Enhance Accessibility: Provide accessible heart health assessment tools.
• Promote Early Detection: Encourage timely interventions for better outcomes.

Features

• Robust Machine Learning Models: Predicts heart disease using advanced techniques and multiple data sources, ensuring increased precision.
• ECG Signal and Image-Based Report Analysis: Accommodates both conventional ECG signals and image-based ECG reports for broader utility.
• Demographic Data Integration: Incorporates age, gender, medical history, etc., into the prediction model for personalized risk assessment.
• Cloud-Based Deployment: Scalable and accessible model deployment for widespread use.
• Web Interface: Enables doctors and patients to upload ECG data, access predictions, prescriptions, and diet plans.
• Mobile Application: Facilitates image-based ECG report uploads for convenient predictions on the go.
• Medical Chatbot (Web): Interprets results, answers questions, and offers basic medical guidance tailored to cardiac health.

Technologies

Core Technologies:

• Python: Main programming language
• TensorFlow/Keras: Deep learning model development
• scikit-learn: Additional machine learning tools
• OpenCV: Image processing
• NumPy/Pandas: Data manipulation

Web Development:

• HTML5/CSS3/JavaScript: Web structure, styling, interactivity
• Bootstrap: Responsive design
• Flask/Django: Python web backend
• React: Modern frontend development

Mobile Development:

• React Native: Cross-platform mobile app development

Databases:

• MS SQL Server: Model data storage
• MongoDB: User data and medical history

Integrated Development Environments

• Google Collab: Cloud-based notebooks for model development.
• Visual Studio Code: Lightweight IDE for app and web development.
• Jupyter Notebook: Interactive environment for data analysis.
• Anaconda: Python distribution platform for virtual environments.

Installation and Setup

1. Dependencies: List all required Python libraries, frameworks, and any external tools. Use a requirements.txt file to manage dependencies with versions if possible.
2. Environment Setup: Guide users on setting up a virtual environment (recommended).
3. Installation Sequence: Provide step-by-step instructions on installing the project and its dependencies.
4. Database Setup: If applicable, explain database configuration and connection process.

Usage Instructions

• Web Interface:
  • Login/Signup: Describe user authentication processes.
  • Uploading ECG Data Support for different file formats (signals, image reports).
  • Viewing Predictions: How the prediction results, prescriptions, and diet plans are presented.
  • Chatbot Interaction: Explain how to access the chatbot and the types of queries it can answer.
• Mobile App:
  • Image Upload: File format support and upload process.
  • Prediction Display: How the mobile app shows the results.
• Command-line Tools (if applicable): Describe relevant commands, their arguments, and expected outputs.

Architecture

Data Collection and Preprocessing

• Data Sources:
  • ECG Data: Specify where you’ll retrieve ECG signals (e.g., PhysioNet WFDB database, other public datasets, internal clinical data).
  • Demographic Data: Explain how this data is collected, formatted, and stored.
• Preprocessing Techniques:
  • Noise Removal: Detail the filtering algorithms employed.
  • Signal Segmentation: Methods used to divide ECG signals into individual heartbeats.
  • Image Processing: Describe any OCR or other techniques used to extract data from ECG image reports.
  • Feature Extraction: Important features derived from the data for model training.

Model Development and Training

• Model Architecture:
  • Type of Models: (e.g., convolutional neural network, recurrent neural network, decision trees, etc.).
  • Rationale: Briefly justify the choice of model architecture.
• Training Process:
  • Dataset Split: Percentages used for training, validation, and testing.
  • Loss Function: The metric the model optimizes during training.
  • Hyperparameters: List important hyperparameters and describe any tuning strategies.
• Performance Evaluation:
  • Metrics: (e.g., accuracy, precision, recall, F1-score, AUC-ROC).
  • Validation Methods: (Cross-validation, holdout set).

Deployment

• Cloud Platform: (e.g., AWS, Microsoft Azure, Google Cloud Platform)
• Containerization: Use of tools like Docker for packaging the application.
• API Development: (If applicable) Describe endpoints for the web/mobile applications to interact with.
• Scaling Considerations: Potential strategies to handle increased load.

Results and Evaluation

• Quantitative Results: Share performance metrics achieved on the test set.
• Benchmarks: Compare against other published results or baselines.
• Discussion: Analyze the significance of the results and potential limitations.

Contributing

This project was developed by Asad Ali in participation with Muhammad Haroom Shahzad and Asad ur Rehman. A project submitted in partial fulfilment of BS Computer Science degree at COMSATS University Lahore.

More Details

For more details, please see the documentation folder.

Let’s Connect

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