Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection

Not provided in the abstract

quantum machine learningbiomarkerscancer detectionfeature selection

2608.19304

Builder Relevance

70%

2h ago

Abstract

This study evaluates quantum-classical hybrid machine learning for lung cancer detection using DNA fragmentomics and DNA methylation.

Reality Card

Core Claim

Quantum kernel methods can effectively capture nonlinear cfDNA fragmentation structure for lung cancer detection, achieving competitive performance compared to classical SVM models.

Method / Result

Quantum-kernel models improved AUC relative to a classical SVM baseline in several 20-feature configurations.

Limitations

Increasing features from 20 to 40 did not consistently improve performance and often increased variability.

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