XTRACTIS FOR PREDICTIVE MEDECINE

Foetus

CARDIOTOCOGRAPHIC IDENTIFICATION OF FETAL HEART PATHOLOGIES

BENCHMARK: XTRACTIS VS. RANDOM FORESTS, BOOSTED TREES & NEURAL NETWORKS

This study aims to solve the following problem:

How to make an automated medical diagnosis of fetal heart disease from signal characteristics of fetal heart rate and uterine contractions?

GOALS

  • Decrease prenatal mortality.
  • Help the medical profession make an earlier and more individualized decision, thanks to rapid and systematic diagnoses.
  • Avoid possible neurological sequelae for the fetus.
 
XTRACTIS RESULTS
 
A predictive model that is:
  • Intelligible. 56 gradual unchained rules, based on 18 predictors.
  • Robust. Excellent predictive performance.
  • Efficient. Instant prediction (online or offline).

CONTENTS

  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Prediction for 1 Unknown Case of Testing
  5. Top-IVE Benchmark
Results by XTRACTIS® GENERATE 12.1.41978 (May 2022)
Use Case first version: May 2022
Current Version: May 2022