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

2026-04-15

AI Summary

This study addresses the clinically challenging problem of anastomotic leakage following colorectal cancer surgery—a rare yet high-risk complication that classical predictive models struggle to identify due to its low incidence and class imbalance. To overcome this limitation, the work introduces quantum machine learning for the first time in this domain, employing a ZZFeatureMap for quantum feature encoding combined with variational quantum circuits based on RealAmplitudes and EfficientSU2 ansätze. The model is trained in a noisy simulation environment with optimization guided by the Fβ metric to prioritize sensitivity. Experimental results demonstrate that the proposed quantum model achieves a sensitivity of 83.3%, significantly outperforming classical baselines (66.7%), thereby validating the advantage of quantum feature spaces in enhancing minority-class detection under low-prevalence conditions.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• anastomotic leak
• colorectal cancer
• risk prediction
• class imbalance
• clinical decision support

Innovation

• Quantum Neural Networks
• Anastomotic Leak Prediction
• Quantum Feature Map
• Noisy Quantum Simulation
• Imbalanced Medical Classification