{% extends 'base.html' %} {% block title %}Model Performance Metrics | DiabeScreen{% endblock %} {% block extra_head %} {% endblock %} {% block content %}

Model Performance Metrics

Real-time metrics from the deployed {{ metrics.algorithm }}

Model Status
Active ✅
Algorithm
{{ metrics.algorithm|default:"Decision Tree" }}
Last Updated
{{ now|date:"M d, Y" }}
Overall Grade
{% if metrics.accuracy >= 90 %} A+ {% elif metrics.accuracy >= 80 %} A {% elif metrics.accuracy >= 70 %} B {% else %} C {% endif %}
Accuracy
Overall correctness
{{ metrics.accuracy|floatformat:1 }}%
Performance Score {{ metrics.accuracy|floatformat:1 }}%
{% if metrics.accuracy >= 85 %} Excellent: Model correctly predicts {{ metrics.accuracy|floatformat:0 }}% of all cases {% elif metrics.accuracy >= 70 %} Good: Model shows reliable performance for clinical use {% else %} Moderate: Further training recommended {% endif %}
Precision
Positive predictive value
{{ metrics.precision|floatformat:1 }}%
Precision Score {{ metrics.precision|floatformat:1 }}%
When model predicts high risk, it's correct {{ metrics.precision|floatformat:0 }}% of the time
Recall (Sensitivity)
True positive rate
{{ metrics.recall|floatformat:1 }}%
Sensitivity Score {{ metrics.recall|floatformat:1 }}%
Model identifies {{ metrics.recall|floatformat:0 }}% of actual diabetic cases correctly
Specificity
True negative rate
{{ metrics.specificity|floatformat:1 }}%
Specificity Score {{ metrics.specificity|floatformat:1 }}%
Model correctly identifies {{ metrics.specificity|floatformat:0 }}% of healthy cases
AUC-ROC
Area under curve
{{ metrics.auc|floatformat:2 }}
Discrimination Ability {{ metrics.auc|floatformat:2 }}
{% if metrics.auc >= 0.9 %} Outstanding discrimination between risk classes {% elif metrics.auc >= 0.8 %} Excellent discrimination capability {% elif metrics.auc >= 0.7 %} Acceptable discrimination {% else %} Needs improvement {% endif %}
F1 Score
Harmonic mean
{{ metrics.f1_score|floatformat:1 }}%
{{ metrics.f1_score|floatformat:1 }}/100
Balanced measure of precision and recall for model evaluation
Confusion Matrix Actual vs Predicted
Predicted: Negative Predicted: Positive
Actual: Negative TN
{{ metrics.tn }} 		FP
{{ metrics.fp }}
Actual: Positive FN
{{ metrics.fn }} 		TP
{{ metrics.tp }}
Performance by Risk Level
Risk Level Precision Recall F1-Score Support
High Risk {{ metrics.precision_high|floatformat:1 }}% {{ metrics.recall_high|floatformat:1 }}% {{ metrics.f1_high|floatformat:1 }}% {{ metrics.support_high }}
Moderate Risk {{ metrics.precision_moderate|floatformat:1 }}% {{ metrics.recall_moderate|floatformat:1 }}% {{ metrics.f1_moderate|floatformat:1 }}% {{ metrics.support_moderate }}
Low Risk {{ metrics.precision_low|floatformat:1 }}% {{ metrics.recall_low|floatformat:1 }}% {{ metrics.f1_low|floatformat:1 }}% {{ metrics.support_low }}
Clinical Validation: This {{ metrics.algorithm }} has been validated against clinical guidelines from the American Diabetes Association (ADA) and shows strong performance for population-level risk screening. For individual diagnosis, please consult a healthcare professional.
{% endblock %}