AI-Based Disease Prediction System: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop an AI-based system that predicts diseases based on user-provided symptoms or medical data.
Scope of the system: Applicable for hospitals, clinics, and personal health monitoring.
2. Requirements Analysis
Functional Requirements:
· - Accept input data such as symptoms, medical history, or test results.
· - Provide disease predictions with probability scores.
· - Offer recommendations for further diagnosis or treatment.
· - Support user registration and secure data storage.
Non-Functional Requirements:
· - High accuracy and reliability in predictions.
· - Scalability to handle a large number of users and data.
· - Secure handling of sensitive medical data.
3. System Design
Architecture:
· - Modular design with components for data input, AI prediction, and result display.
· - Integration with healthcare databases for enriched insights.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of user interactions with the system.
· - Level 1: Modules like Data Input, Prediction Engine, and Recommendation System.
Database Design:
· - Tables: Users, Symptoms, Medical Records, Predictions.
4. Technology Stack
Frontend:
· - Web or mobile app using React.js, Angular, or Flutter.
Backend:
· - Python (Flask/Django) or Node.js for API development.
· - AI/ML Frameworks: TensorFlow, PyTorch, or Scikit-learn for predictive modeling.
Database:
· - SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase).
Cloud Services:
· - AWS, Google Cloud, or Azure for deployment and scalability.
5. Implementation
Data Collection:
· - Gather datasets with symptoms and corresponding disease labels.
· - Preprocess data to ensure quality and consistency.
Model Development:
· - Train machine learning models for disease prediction.
· - Evaluate models for accuracy, precision, and recall.
Frontend Development:
· - Design user-friendly interfaces for data input and result display.
· - Implement visualizations for prediction probabilities.
Backend Development:
· - Develop APIs for data input, prediction, and recommendations.
· - Implement data storage and security mechanisms.
Integration:
· - Connect frontend, backend, and AI models for seamless functionality.
6. Security
Encrypt data during storage and transmission using protocols like TLS.
Implement user authentication and authorization mechanisms.
Ensure compliance with data protection regulations like HIPAA or GDPR.
7. Testing
Unit Testing: Validate individual components like prediction models and APIs.
Integration Testing: Ensure smooth interaction between frontend, backend, and AI models.
System Testing: Test the system with real-world data inputs.
Performance Testing: Evaluate system response times and prediction accuracy.
8. Deployment
Deploy the system on cloud platforms for scalability and reliability.
Set up monitoring tools for system performance and issue detection.
Provide user training and support for initial adoption.
9. Maintenance and Updates
Regularly update prediction models with new medical data.
Monitor system logs and user feedback to identify and resolve issues.
Incorporate advancements in AI and healthcare for system enhancements.
10. Appendix
Glossary of terms.
References and additional resources.