Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucial
role in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clustering
method by integrating a quantum computing approach using Manhattan distance to improve
prediction accuracy for lung cancer diagnosis. The research was conducted using a publicly available
lung cancer dataset consisting of 309 patient records with 14 diagnostic attributes. Comparative experiments
were carried out between the classical K-Medoids and the quantum-enhanced K-Medoids, with
performance evaluated based on clustering accuracy, precision, recall, and F1-score. The results show
that the quantum-based method has the same accuracy as the classical method, namely 88%. This
suggests that quantum-based clustering can match the accuracy of classical methods after adequate
training, although consistency and parameter stability remain areas for further refinement. Further
research is recommended to test the model on larger datasets and to explore real-world deployment in
clinical decision support systems.