Result Visualization

Effective result visualization bridges the gap between predictive output and human understanding. This interface uses targeted UI elements to communicate cancer risk clearly and responsibly.

Why Visualization Matters

Medical tools must go beyond binary labels. Patients and clinicians benefit from visual cues that show how confident a model is, what it sees as important, and how risk levels compare to known benchmarks. Visualizations support:

  • Better interpretation of prediction certainty
  • Faster comprehension of risk levels
  • Trust-building through transparency

Probability Meter

A horizontal “risk bar” presents the predicted probability in a single glance, transitioning from green (benign) to red (malignant).

The exact probability (e.g., 0.8514) is also shown in text beside the bar, combining visual and numeric clarity.

Class Distribution Chart

To contextualize an individual prediction, the system includes a bar chart showing the distribution of training data:

Training Class Balance

  • Benign: 357 samples
  • Malignant: 212 samples
Benign Malignant

Diagnosis Panel

The model's final verdict is shown in a styled callout that changes based on the prediction result.

Benign

The input profile is likely non-cancerous.

Malignant

The input profile resembles known malignant samples.

UX Considerations

Design decisions for the result interface emphasized clarity, responsiveness, and reassurance:

  • Animated transitions draw attention without distraction.
  • Contrast ratios and font sizing follow accessibility guidelines.
  • Responsive layout supports both desktop and mobile viewing.

Key Takeaways

  • Probability and diagnosis are visualized with both text and color-based cues.
  • Training set context and dynamic risk feedback improve interpretability.
  • The layout is optimized for clarity and confidence in clinical decision-making.