Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data

Stroke is a severe medical condition that occurs when the blood supply to parts of the brain is interrupted or reduced, resulting in brain tissue that lacks oxygen and nutrients. This causes brain cells to start to die in minutes. Early prevention reduces the risk of stroke. In this study, a quantum computing approach is used to improve the performance of the K-Medoids method. A comparative analysis of these methods was carried out with a focus on their performance, especially on the accuracy of the test results. The investigation was carried out using a data set of stroke patient medical records. The data set was tested using the classical and K-Medoids methods with a quantum computing approach utilizing Manhattan distance calculations. The findings of this research reveal improvements in the K-Medoids algorithm with Manhattan distance calculation influenced by the integration of a quantum computing framework. In particular, the simulation test results show an increase in accuracy from the classical K-Medoids method to the K-Medoids method with a quantum computing approach, from 52% to 64%. These results highlight that the performance of the K-Medoids method with a quantum computing approach is superior to that of the classical K-Medoids method.