XTRACTIS FOR PREDICTIVE MEDECINE

GENETIC DIAGNOSIS OF PROSTATE CANCER

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

This study aims to solve the following problem:

How to make a reliable automated medical diagnosis of prostate cancer from genetic sequencing of prostate tissue? 

GOALS

  • Enhance medical knowledge by helping urologists and oncologists understand the causal relationships between specific genes and the presence of cancer.
  • Assist the medical profession in making an earlier and more individualized decision, thanks to rapid and systematic diagnoses.
  • Contribute to improving patient care (pain, survival, duration of treatment) and access to diagnostics even in medical deserts.

 
XTRACTIS RESULTS
 
A predictive model that is:
  • Intelligible. 4 gradual unchained rules, based on 7 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 3 Unknown Cases of Testing
  5. Top-IVE Benchmark
Results by XTRACTIS® GENERATE 11.2.38531 (June 2021)
Use Case first version: June 2018
Current Version: May 2022