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

• URL: http://arxiv.org/abs/2604.13951v1
• Date: Wed, 15 Apr 2026 15:03:37 GMT
• Title: Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
• Abstract summary: This study evaluates colorectal risk factors and classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction.<n>$F_$-optimized quantum configurations yielded significantly higher sensitivity (83.3%) than classical baselines.
• Score: 0.049259062564301744
• License: http://creativecommons.org/licenses/by/4.0/
• 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. $F_β$-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.

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