Data Mining in Clustering of Patients Diseases Using K-Medoids Method
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Abstract
Disease is a condition in which the mind and body experience a kind of disturbance, discomfort for those who experience it. Day by day, the number of patients at the Kuok Health Center is increasing with various types of different diseases. The increase number of patients requires the Kuok Health Center staff always update the patient's medical record data. The patient's medical record data is the form of a report containing the number of patients and their illnesses. Based on these data, the Puskesmas needs to find out information about the diseases that are most vulnerable and suffered by many patients. This study aims to classify patient disease data to find out the most common diseases suffered by patients at the Kuok Health Center, Kampar Regency. The grouping of patient disease data is carried out with the Data Mining Clustering and followed by the K-Medoids method. Next, cluster testing is carried out using the Silhouette Coefficient. The results of this study indicate that in cluster 1 the most common disease suffered by patients is non-insulin dependent diabetes mellitus (type II) with a total of 435 cases. In cluster 2, the most common disease suffered by patients was Essential Hypertension (Primary) with a total of 2785 cases. For cluster 3, the most common disease suffered by patients was Vulnus Laseratum, Punctum, with a total of 328 cases. From the cluster results obtained, the results of the Silhouette Coeficient test are 0.900033674.
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References
[2] Sundari, S., Damanik, I. S., Windarto, A. P., Tambunan, H. S., Jalaluddin, J., & Wanto, A. (2019). Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia. Prosiding Seminar Nasional Riset Information Science (SENARIS), 1, 687. doi:10.30645/senaris.v1i0.75
[3] Irwansyah, E., Salim Pratama, E., & Ohyver, M. (2020). Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids. doi:10.20944/preprints202008.0074.v
[4] Lee, M., Lee, S., Park, J., & Seo, S. (2020). Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm. Animals, 10(8), 1348. doi:10.3390/ani10081348
[5] Samudi, S., Widodo, S., & Brawijaya, H. (2020). The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic. SinkrOn, 5(1), 116. doi:10.33395/sinkron.v5i1.10649
[6] Rifa, I. H., Pratiwi, H., & Respatiwulan, R. (2020). Clustering Of Earthquake Risk in Indonesia Using K-Medoids and K-Means Algorithms. Media statistika, 13(2), 194–205. doi:10.14710/medstat.13.2.194-205
[7] Yan, H., Yang, N., Peng, Y., & Ren, Y. (2020). Data mining in the construction industry: Present status, opportunities, and future trends. Automation in Construction, 119, 103331. doi:10.1016/j.autcon.2020.103331
[8] Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716–80727. doi:10.1109/access.2020.2988796
[9] Rahman, F., Ridho, I. I., Muflih, M., Pratama, S., Raharjo, M. R., & Windarto, A. P. (2020). Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country. IOP Conference Series: Materials Science and Engineering, 835, 012058. doi:10.1088/1757-899x/835/1/012058
[10] Hooshyar, D., Yang, Y., Pedaste, M., & Huang, Y.-M. (2020). Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access, 8, 146994–147014. doi:10.1109/access.2020.3014948
[11] Muruganandham, S. K., Sobya, D., Nallusamy, S., Mandal, D. K., & Chakraborty, P. S. (2018). Study on Leaf Segmentation Using K-Means and K-Medoid Clustering Algorithm for Identification of Disease. Indian Journal of Public Health Research & Development, 9(5), 289. doi:10.5958/0976-5506.2018.00456.4
[12] Desi Asima Silitonga., Agus Perdana Windarto., & Dedy Hartama, Sumarno. (2019). Penerapan Metode K-Medoid pada Pengelompokan Rumah Tangga Dalam Perlakuan Memilah Sampah Menurut Provinsi. Seminar Nasional Sains & Teknologi Informasi (SENSASI)., ISBN: 978-602-52720-2-8 Hal: 313 – 318