Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913035387404288 |
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| author | Novák, Vojtěch Zelinka, Ivan Přibylová, Lenka Martínek, Lubomír Benčurík, Vladimír Beseda, Martin |
| author_facet | Novák, Vojtěch Zelinka, Ivan Přibylová, Lenka Martínek, Lubomír Benčurík, Vladimír Beseda, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13951 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors Novák, Vojtěch Zelinka, Ivan Přibylová, Lenka Martínek, Lubomír Benčurík, Vladimír Beseda, Martin Machine Learning Quantum Physics 68T07, 81P68, 62P10, 68Q12 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. |
| title | Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors |
| topic | Machine Learning Quantum Physics 68T07, 81P68, 62P10, 68Q12 |
| url | https://arxiv.org/abs/2604.13951 |