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Background <p>Lung cancer is a leading cause of cancer mortality, and early detection is critical for patient outcomes. Support Vector Machines (SVM) have been widely applied to diagnostic classification, but their performance can be limited in terms of sensitivity. Quantum Support Vector Machines (QSVM) offer a potential approach to improve this aspect.</p> Methods <p>We compared SVM and QSVM on balanced lung cancer subsets (1,000 samples each) using k-fold cross-validation. QSVM employed ZZFeatureMap and FidelityQuantumKernel for quantum feature encoding.</p> Results <p>QSVM achieved an average recall improvement of about 8% over classical SVM, while maintaining comparable precision and accuracy. Under a realistic 1:100 class imbalance, precision declined, highlighting challenges with highly skewed data.</p> Conclusion <p>QSVM shows promise for enhancing sensitivity in medical diagnostics but requires further validation on larger, heterogeneous datasets and real quantum hardware.</p> Clinical trial number <p>Not applicable.</p>

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Overcoming SVM limitations in lung cancer classification with a quantum feature map

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • A. Toufah,
  • M. A. Kadim,
  • My Y. El Hafidi

摘要

Background

Lung cancer is a leading cause of cancer mortality, and early detection is critical for patient outcomes. Support Vector Machines (SVM) have been widely applied to diagnostic classification, but their performance can be limited in terms of sensitivity. Quantum Support Vector Machines (QSVM) offer a potential approach to improve this aspect.

Methods

We compared SVM and QSVM on balanced lung cancer subsets (1,000 samples each) using k-fold cross-validation. QSVM employed ZZFeatureMap and FidelityQuantumKernel for quantum feature encoding.

Results

QSVM achieved an average recall improvement of about 8% over classical SVM, while maintaining comparable precision and accuracy. Under a realistic 1:100 class imbalance, precision declined, highlighting challenges with highly skewed data.

Conclusion

QSVM shows promise for enhancing sensitivity in medical diagnostics but requires further validation on larger, heterogeneous datasets and real quantum hardware.

Clinical trial number

Not applicable.