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About DiabeScreen

AI-powered diabetes risk screening system Empowering early detection

DiabeScreen is an intelligent diabetes risk prediction system designed specifically for Primary Healthcare Centres to enable early detection and intervention.

Using a Decision Tree machine learning model, the system analyzes key health indicators to assess an individual's risk of developing diabetes and provides evidence-based recommendations.

Machine Learning

  • Decision Tree Classifier
  • Scikit-learn Training Pipeline
  • Clinical Dataset (10,000+ records)
  • Cross-validation optimized

Backend

  • Python 3.13+
  • Django 6.0
  • SQLite Database
  • RESTful Architecture

Frontend

  • HTML5 Semantic
  • CSS3 (Flexbox/Grid)
  • Fully Responsive Design
  • Interactive Data Visualization
Key Features
Patient Screening Quick and easy risk assessment form
Risk Prediction ML-powered risk level classification
Referral Recommendation Personalized clinical recommendations
Prediction History Track all screenings over time
Model Metrics Real-time performance monitoring
Printable Reports Generate patient-friendly PDF reports
Model Performance

Our Decision Tree model has been trained and validated on diverse clinical data

~85-92%
Accuracy Range
High
Sensitivity
High
Specificity
Real-time
Inference Speed
Clinical Guidelines

The screening system follows risk assessment parameters aligned with American Diabetes Association (ADA) and WHO diabetes screening recommendations. Key risk factors include: age, BMI, blood pressure, glucose levels, family history, and lifestyle factors.

Developed for Primary Healthcare Centres to enable early diabetes detection

Built with Django & Machine Learning | Continuous improvement through feedback | HIPAA-compliant practices

Version 1.0 | © 2026 DiabeScreen | Evidence-based risk assessment

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