System Requirements Document

1. Introduction

This document outlines the system requirements for the Heart Disease Prediction System using Machine Learning and AI. The project aims to develop a predictive model that assesses the likelihood of heart disease based on various health parameters. The system leverages machine learning algorithms to provide real-time risk assessments, assisting healthcare professionals and individuals in making informed decisions.

2. System Overview

The Heart Disease Prediction System is designed to predict the risk of heart disease using machine learning algorithms. It processes patient data, including age, blood pressure, cholesterol levels, and other health indicators, to generate predictions. The system is intended for use by healthcare professionals and individuals, providing a user-friendly interface for inputting medical parameters and receiving predictions.

3. Functional Requirements

User Stories

• As a healthcare professional, I want to input patient health parameters to receive a prediction of heart disease risk.
• As a user, I want to enter my health data and receive a real-time assessment of my heart disease risk.
• As a system administrator, I want to manage user access and monitor system performance.
• As a developer, I want to preprocess data to handle missing values and normalize attributes for accurate predictions.
• As a data scientist, I want to apply feature selection techniques to identify key health indicators affecting heart disease risk.
• As a user, I want to view a risk score percentage indicating the probability of developing heart disease.
• As a healthcare professional, I want to understand which health factors contributed most to the risk assessment.
• As a user, I want to download a PDF report of my prediction results and health recommendations.
• As a system administrator, I want to ensure data security and compliance with healthcare regulations.
• As a developer, I want to integrate Explainable AI techniques to provide transparency in model predictions.
• As a user, I want to access the system via a web-based application, desktop application, or mobile app.
• As a healthcare professional, I want to receive alerts for high-risk patients for timely intervention.
• As a user, I want to receive personalized health recommendations based on my risk assessment.
• As a developer, I want to implement multiple machine learning algorithms and select the best-performing model.
• As a system administrator, I want to track user activity and system logs for security and debugging purposes.
• As a healthcare professional, I want to monitor blood pressure and cholesterol levels as part of the health parameters.
• As a developer, I want to implement Logistic Regression, Decision Trees, Random Forest, and SVM algorithms for prediction.
• As a user, I want to understand the system's hardware and software specifications for optimal use.
• As a developer, I want to manage patient data and prediction results using a well-structured database design.
• As a system administrator, I want to ensure the system is scalable and can be deployed on platforms like Google Cloud.

4. User Personas

• Healthcare Professional: Uses the system to assess patient risk and make informed decisions.
• Individual User: Inputs personal health data to receive risk assessments and recommendations.
• System Administrator: Manages user access, monitors system performance, and ensures data security.
• Developer/Data Scientist: Develops and maintains the machine learning models and system infrastructure.

5. Core User Flows

Healthcare Professional

1. Log in to the system.
2. Input patient health parameters.
3. Receive risk assessment and contributing factors.
4. Download PDF report for patient records.

Individual User

1. Access the system via web or mobile app.
2. Enter personal health data.
3. View real-time risk assessment and recommendations.
4. Download a personalized health report.

System Administrator

1. Log in to the admin panel.
2. Manage user accounts and permissions.
3. Monitor system logs and performance metrics.
4. Ensure compliance with data security regulations.

6. Visuals Colors and Theme

• Primary Color: Blue (for trust and professionalism)
• Secondary Color: Green (for health and wellness)
• Accent Color: Red (for alerts and high-risk notifications)

7. Signature Design Concept

• Clean and intuitive interface with easy navigation.
• Use of charts and graphs for visual representation of data.
• Responsive design for accessibility on various devices.

8. Interaction Model & Motion Direction

• Interactive forms with real-time validation.
• Smooth transitions between input and result screens.
• Hover effects for interactive elements.

9. Non-Functional Requirements

• Performance: The system should handle multiple concurrent users without significant delays.
• Security: Ensure data encryption and secure access control.
• Scalability: The system should support integration with additional datasets and models.
• Usability: The interface should be user-friendly and accessible to non-technical users.

10. Tech Stack

• Frontend: Python, Django Framework
• Backend: SQLite
• Development Environment: Pycharm
• Operating System: Windows 10 Home
• Browser Compatibility: Chrome, Microsoft Edge

11. Assumptions and Constraints

• The system assumes access to quality datasets for training and testing.
• The system is constrained by the computational power of the hardware used.
• Data privacy and compliance with regulations such as HIPAA and GDPR are mandatory.

12. Glossary

• Machine Learning (ML): A method of data analysis that automates analytical model building.
• Support Vector Machine (SVM): A supervised machine learning algorithm used for classification.
• Explainable AI (XAI): Techniques that make the output of AI models understandable to humans.
• ROC-AUC: Receiver Operating Characteristic - Area Under Curve, a performance measurement for classification problems.
• Don Bosco College: The institution where the project was developed.
• Department of Computer Science: The department overseeing the project.
• Aravindhan C: The author of the project.
• Bosco College: Refers to Don Bosco College.
• System Study: Analysis of existing and proposed systems.
• Existing System: Traditional methods of heart disease diagnosis.
• Proposed System: The new machine learning-based prediction system.
• Future Enhancement: Potential improvements and additional features for the system.