AI RESEARCH
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
arXiv CS.LG
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ArXi:2604.13951v1 Announce Type: new 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