Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors

TL;DR

This paper compares classical and quantum machine learning models for predicting colorectal anastomotic leaks, showing quantum models achieve higher sensitivity in low-prevalence clinical data.

Contribution

It demonstrates the effectiveness of quantum neural networks in clinical risk prediction, especially for minority class detection, under noisy simulated conditions.

Findings

Quantum models achieved 83.3% sensitivity versus 66.7% for classical models.

Quantum feature spaces better identify minority class cases.

Analysis of optimizer performance under noise highlights future hardware challenges.

Abstract

This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14\% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. $F_\beta$-optimized quantum configurations yielded significantly higher sensitivity (83.3\%) than classical baselines (66.7\%). This demonstrates that quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.