Case 04 · Healthcare ML · Provis Technologies internship
Parkinson's Voice Classifier
Screening support from the voice — with the decision threshold left in the clinician's hands.
The problem
In screening, the two mistakes aren't equal: missing a real case costs far more than a false alarm. A single fixed cut-off hides that trade-off from the person who has to make the call.
What I built
- An XGBoost classifier trained on 22 voice-derived acoustic features.
- A Streamlit decision-support dashboard with adjustable decision thresholds.
- Batch CSV scoring, feature-importance visualisation and exportable predictions.
- Tuned specifically to cut false negatives — the costlier error in diagnosis.
22acoustic features
12%fewer false negatives after tuning
BatchCSV scoring + export
Livethreshold control