<![CDATA[Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucialrole in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clusteringmethod by integrating a quantum computing approach using Manhattan distance to improveprediction accuracy for lung cancer diagnosis. The research was conducted using a publicly availablelung cancer dataset consisting of 309 patient records with 14 diagnostic attributes. Comparative experimentswere carried out between the classical K-Medoids and the quantum-enhanced K-Medoids, withperformance evaluated based on clustering accuracy, precision, recall, and F1-score. The results showthat the quantum-based method has the same accuracy as the classical method, namely 88%. Thissuggests that quantum-based clustering can match the accuracy of classical methods after adequatetraining, although consistency and parameter stability remain areas for further refinement. Furtherresearch is recommended to test the model on larger datasets and to explore real-world deployment inclinical decision support systems.]]>
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