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arXiv · 2604.13951

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

Vojtěch Novák·Ivan Zelinka·Lenka Přibylová·Lubomír Martínek·Vladimír Benčurík·Martin Beseda
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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.

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Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, Vladimír Benčurík, Martin Beseda. 2026-04-15. Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors. https://arxiv.org/abs/2604.13951

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