New Innovation: Predicting Anemia with the K-Medoids Method and Quantum Computing Using Manhattan Distance

Abstract

The low accuracy of anemia diagnosis with the classical K-Medoids method shows the need for alternative, more effective techniques in processing medical record data. This research aims to analyze the effectiveness of the quantum computing approach as a solution to develop an anemia diagnostic method by integrating the K-Medoids algorithm and Manhattan distance calculation. This research is an experimental study with a comparative design. The research subjects comprised anemia patient medical record data covering 5 attributes and 1 target, with 20 samples taken from the Kaggle.com platform. Data collection was conducted using data mining techniques, while the instrument used was computational modeling software. The data was analyzed using the accuracy comparison method between the classical and quantum computing-based K-Medoids methods. The analysis results show that the quantum computing-based K-Medoids method can achieve 80% accuracy, which is equivalent to the classical K-Medoids method, but with higher data processing efficiency. This research confirms that integrating quantum computing in the K-Medoids method can be an alternative in diagnosing anemia, offering the potential for broader application to more complex medical record data. The implication of this research is the creation of opportunities for innovation in quantum computing-based medical decision support systems that are more efficient.