Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
For colorectal surgery risk assessment, this demonstrates that quantum feature spaces can improve minority class detection in low-prevalence clinical prediction.
This study shows that Quantum Neural Networks achieve 83.3% sensitivity for anastomotic leak prediction, outperforming classical models (66.7%) on a dataset with 14% leak prevalence.
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. $F_β$-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.