🌴 **Diabetes Predictor** 🌴

A Tool for Diabetes Analysis, Predictions

View Demo · Installation · Project Report

Built with 🤍 by Debarghya Chakravarty, Raktim Bar, Supriyo Bose, Swathik Majumder, Tunir Chakraborty

through Prof. Somen Hati --(Department Of CSE)

🚀 A Project Submitted for the partial fulfilment of the degree B.Tech CSE at Academy Of Technology during academic year 2023-24

Diabetes Prediction Project

This project employs machine learning techniques to address the global health challenge of rising diabetes prevalence. Utilizing algorithms like logistic regression, support vector machines, and neural networks, it analyzes a comprehensive dataset including age, BMI, family history, and glucose levels. The goal is to develop an accurate predictive model identifying individuals at risk, enabling proactive healthcare interventions. Optimization methods include feature selection, ensemble techniques, and deep learning architectures, ensuring model interpretability and practicality in healthcare settings. Emphasizing early intervention, the project aims to reduce diabetes-related complications through personalized strategies. The report provides insights into algorithm analysis, data preprocessing, and user-friendly interfaces, aiming to facilitate proactive healthcare strategies and alleviate the burden of diabetes.

Feasibility Study

The feasibility study evaluates technical, economic, and operational aspects to determine implementation viability. Technical assessment involves dataset availability and computational resources. Economic analysis considers expenses versus healthcare benefits. Operational evaluation ensures seamless integration into existing systems.

System Analysis

System analysis examines the proposed scheme's components and functionalities. It integrates robust machine learning algorithms, preprocesses the dataset, applies feature selection techniques, and designs a user-friendly interface. Emphasis is placed on scalability and adaptability to diverse healthcare settings.

Software Requirements Specification

Intended Audience and Reading Suggestions

1. Academic Evaluators and Reviewers
2. Project Supervisors and Mentors
3. Software Developers and Data Scientists
4. Healthcare Professionals
5. Researchers in Healthcare and Machine Learning

Product Scope

The project aims to develop a machine learning-based system capable of predicting the risk of diabetes in individuals using historical health data.

Product Features

1. Data Collection Module
2. Data Preprocessing
3. Feature Selection/Extraction
4. Machine Learning Model Development
5. Model Training and Validation
6. Prediction and Risk Assessment
7. Results Interpretation and Visualization
8. Alerts and Notifications
9. Model Performance Monitoring
10. Privacy and Security Measures
11. Documentation and Reporting
12. Scalability and Adaptability

Operating Environment

System Design

The system architecture consists of various modules including Data Acquisition, Preprocessing, Feature Engineering, Machine Learning Model Development, Diabetes Prediction, and User Interface.

Relationships

• Patients and their medical records
• Predictive model data and patient outcomes

Overall System Architecture

1. Data Acquisition and Preprocessing Module
2. Feature Engineering Module
3. Machine Learning Model Training and Evaluation Module
4. Diabetes Prediction Module
5. User Interface Module

Data Flow

1. Patient data collection
2. Data preprocessing
3. Feature extraction
4. Model training and evaluation
5. Diabetes prediction
6. User interface interaction

Non-Functional Requirements

• Accuracy

• Explainability

• Scalability

• Security

• Usability

  

Design and Implementation Constraints

Challenges include sourcing diverse, high-quality health data, complying with privacy regulations, ensuring model interpretability, scalability concerns, and optimal algorithm selection within computational limitations.

Assumptions and Dependencies

Assumptions include the availability of diverse and reliable health data, adherence to regulatory standards, and consistent access to computational resources. Dependencies lie in inaccurate data preprocessing, algorithm performance, and seamless integration with healthcare systems.

Tools & Technologies

Development Tools:

• Visual Studio Code (VS Code): A free and open-source code editor developed by Microsoft.
• Google Colab: A cloud-based Jupyter Notebook environment for writing and executing Python code.
• Anaconda: A popular Python distribution for data science and machine learning.
• Git: A distributed version control system for tracking changes in source code.
• Git Bash: A command-line environment for working with Git on Windows.
• Figma: A cloud-based interface design tool for creating user interfaces.
• Canva: A free online graphic design tool for creating visual content.

Languages & Libraries:

• Python: A high-level, general-purpose programming language.
• Streamlit: An open-source Python library for creating web apps for machine learning and data science.
• Shell Script: A scripting language for automating tasks in Unix-based systems.

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