Cardiotocographic Identification of Fetal Heart Conditions

Benchmark vs. Logistic Regression, Random Forest, Boosted Tree & Neural Network

Design an AI-based decision system that accurately and instantly makes a rational medical diagnosis of fetal heart condition from signal characteristics of fetal heart rate and uterine contractions.

Goals & benefits

Identify parameters requiring increased vigilance and improve medical knowledge by helping cardiologists understand the causal relationships between specific cardiotocographic features, their combination, and the presence of an abnormality.

Help the medical profession to make earlier and more personalized decisions by means of rapid, systematic, and explainable diagnoses. Extend access to high-level diagnoses even in medical deserts.

Decrease prenatal mortality and avoid possible neurological sequelae for the fetus.

  • The top-model is a decision system composed of 56 gradual rules without chaining, aggregated into 10 disjunctive rules.
  • Each rule uses from 2 to 8 predictors among the 18 variables that XTRACTIS automatically identified as significant (out of the 21 potential predictors characterizing the fetal cardiotocograms and uterine contraction signals).
  • Only a few rules are triggered at a time to compute the decision.

It has a good Real Performance (on unknown data).

It computes real-time predictions up to 70,000 decisions/second, offline or online (API).

UC03 scores graph

LoR=Logistic Regression
RFo=Random Forest
BT=Boosted Tree
NN=Neural Network

Detailed results and explanations in full document

Use Case 2024/03 (v3.0)

Powered by XTRACTIS® REVEAL v12.1.41978 (2022/05)


  1. Problem Definition
  2. XTRACTIS-induced Decision System
  3. XTRACTIS Process
  4. Top-Model Induction
  5. Explained Predictions for 4 unkown cases
  6. Top-Models Benchmark
  7. Appendices