Year: 2025
Type: article
Abstract: 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.
Source: BMC Artificial Intelligence
Authors: A. Toufah, M. A. Kadim, My Y. El Hafidi
Institution: University of Hassan II Casablanca
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,990
OpenAlex ID: W4417154308