XTRACTIS FOR PREDICTIVE MEDECINE
Voice-based Detection of Parkinson’s Disease
Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks
How can an automated — yet totally transparent — medical diagnosis of Parkinson’s disease be made from the patient’s simple voice recordings?
Identify the parameters involved in the Parkinson’s disease and enhance medical knowledge by helping neurologists understand the causal relationships between these parameters, their combination, and the disease.
Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.
Use a model with simple recordings to limit medical protocols that can be costly.
We get a Predictive Model that is:
Intelligible.
A Decision System composed of 26 unchained gradual rules, each using some of the 92 variables that XTRACTIS identified as significant (out of 753 potential predictors characterizing each patient).
Pretty good Real Performance on External Test.
Efficient & Operational.
Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).
Use Case 2023/05 (v1.2)
Results by
XTRACTIS® GENERATE 13.0.45667 (2023/05)
DOCUMENT CONTENTS
- Problem Definition
- Xtractis Solution
- Top-Model Induction
- Explained Predictions for 3 cases
- Top-Models Benchmark
