Serological Diagnosis of Chronic Kidney Disease

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 chronic kidney disease, from the patient record and its blood measures.
Goals & benefits

Identify the parameters involved in the kidney disease and enhance medical knowledge by helping nephrologists 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 few predictors to limit medical data that can be expensive to collect.

  • The top-model is a decision system composed of 4 disjunctive gradual rules without chaining. 
  • Each rule uses from 2 to 6 predictors among the 8 variables that XTRACTIS automatically identified as significant (out of the 24 parameters  describing each patient).
  • Only a few rules are triggered at a time to compute the decision.

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

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

UC14 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 (v2.0)

Powered by XTRACTIS® REVEAL v13.0.44983 (2023/02)


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