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
- Problem Definition
- Xtractis Solution
- Top-Model Induction
- Prediction for 3 Unknown Cases of Testing
- Top-IVE Benchmark
