New Approach K-Medoids Clustering Based on Chebyshev Distance with Quantum Computing for Anemia Prediction - Mochamad Wahyudi; Solikhun Solikhun; Lise Pujiastuti; Gerhard-Wilhelm Weber (Universitas Bumigora, 2025)
Anemia is a condition where the number of red blood cells or hemoglobin levels is below normal, reducing the blood’s ability to carry oxygen, which can lead to symptoms such as fatigue, weakness, and shortness of breath.This study aims to utilize a quantum computing approach to improve the performance of the K-Medoids method by calculating the Chebyshev Distance to predict anemia. The method used is the K-Medoids clustering method with the calculation of the Chebyshev Distance and quantum computing. A comparative analysis of these methods is carried out with a focus on their performance, especially the accuracy of the test results. This study was conducted using a dataset of medical records of patients with anemia. The dataset was taken from Kaggle. This dataset includes five attributes used to predict anemia disease patterns. The dataset was tested using the classical method and K-Medoids with a quantum computing approach that utilizes the Chebyshev Distance calculation. The results of this study reveal a new alternative model for the K-Medoids algorithm with the Chebyshev Distance calculation influenced by the integration of the quantum computing framework. Specifically, the simulation test results show the same accuracy as the classical K-Medoids method and the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations with an accuracy of 80%. The conclusion of this study highlights that the performance of the K-Medoids method with a quantum computing approach with Chebyshev Distance calculations can be implemented to predict anemia using the clustering method.