Parkinson's voice screening, presented with evidence instead of hype.
This platform turns acoustic voice features into a clear research workflow: upload inputs, compare saved models, inspect prediction confidence, and surface the grouped SHAP drivers behind each result.
Audience
Faculty, judges, and research reviewers
Focus
Prediction, interpretability, and model credibility
Live study snapshot
XGBoost
Best accuracy
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Study features
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Capture a voice sample profile
Use structured CSV input or the manual analysis workspace to submit acoustic measurements.
Run the strongest saved model
Compare support for XGBoost and Random Forest without changing the backend contract.
Inspect explainable evidence
Review grouped SHAP drivers, plain-language explanations, and model performance context.
Study posture
A sharper first impression without changing the research workflow.
The redesign keeps the deployed app architecture intact while reframing the interface around trust, readability, and evidence-led presentation.
Dataset
756
Voice-derived study samples available to the deployed interface.
Feature set
22
Selected acoustic indicators surfaced through the upload workspace.
Best accuracy
94.0%
Current headline benchmark from XGBoost.
Workflow
The platform now reads like a guided review instead of a disconnected set of demos.
Each route supports one part of the story: capture inputs, inspect the result, explain the model, and compare saved evaluation benchmarks.
A faculty-ready narrative that connects dataset quality, model choice, and interpretability.
An analysis workflow built to show both plain-language summaries and technical depth.
A restrained interface that foregrounds evidence instead of decorative UI noise.
Analysis workspace
Upload a CSV or enter feature values manually without leaving the review flow.
Clear result framing
Probability, confidence, model context, and next-step links are grouped for fast review.
Evidence trail
Explainability and performance routes turn the prediction into a defensible research narrative.
Explainability
Grouped SHAP outputs keep the science visible.
Instead of hiding the model behind a single percentage, the platform surfaces which families of signal changes drove the score.
Performance framing
Model benchmarks stay readable for non-technical reviewers.
Plain-language metric summaries coexist with the original technical tables so judges can scan first and dive deeper second.
Next step
Open the analysis workspace and evaluate the full prediction-to-explanation flow.
The visual redesign is meant to support the live demo, so the strongest proof is the complete path from feature input to interpreted result.