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...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-10-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5814 |
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| author | Ricardo Siahaan Swingly Purba Jeremia Siregar Marvin Frans Sakti Hutabarat Rasmi Sitohang |
| author_facet | Ricardo Siahaan Swingly Purba Jeremia Siregar Marvin Frans Sakti Hutabarat Rasmi Sitohang |
| author_sort | Ricardo Siahaan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4ce6d6730db54631b55d3bc823cd8572 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-4ce6d6730db54631b55d3bc823cd85722025-01-13T03:31:56ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-10-018562363010.29207/resti.v8i5.58145814Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical DataRicardo Siahaan0Swingly Purba1Jeremia Siregar2Marvin Frans Sakti Hutabarat3Rasmi Sitohang4Institut Sains dan Teknologi TD PardedeInstitut Sains dan Teknologi TD PardedeInstitut Sains dan Teknologi TD PardedeInstitut Sains dan Teknologi TD PardedeInstitut Sains dan Teknologi TD PardedeStroke 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5814clusteringquantum computingdata miningk-medoidssuperposition |
| spellingShingle | Ricardo Siahaan Swingly Purba Jeremia Siregar Marvin Frans Sakti Hutabarat Rasmi Sitohang Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) clustering quantum computing data mining k-medoids superposition |
| title | Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data |
| title_full | Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data |
| title_fullStr | Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data |
| title_full_unstemmed | Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data |
| title_short | Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data |
| title_sort | quantum enhanced k medoids clustering comparative analysis of stroke medical data |
| topic | clustering quantum computing data mining k-medoids superposition |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5814 |
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