Analysis of Tuberculosis Disease Case Growth From Medical Record Data, Viewed Through Clustering Algorithms (Case Study: Islamic Hospital Bogor)
DOI:
https://doi.org/10.59141/jiss.v4i09.884Keywords:
Tuberkulosis, Clustering, K-means, Fuzzy C-Means, Gaussian MixtureAbstract
Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis infection. Tuberculosis can spread from one person to another through airborne transmission. This disease is most commonly found in the Asian region. Currently, Indonesia ranks second after India in terms of tuberculosis cases. The discovery of tuberculosis cases by province in Indonesia reveals that West Java Province is one of the contributors to the highest tuberculosis cases. It is known that the tuberculosis case rate in Bogor Regency is one of the highest in West Java. This serves as the foundation for the focus of this research, which will be conducted at Islamic Hospital Bogor, to determine the average age and gender of patients who are more susceptible to tuberculosis. One way to understand the growth of tuberculosis cases is through clustering using Data Mining Techniques, specifically several clustering algorithms such as k-means clustering, fuzzy c-means, and Gaussian mixture. These techniques aim to identify the growth of tuberculosis cases based on age range and gender. Therefore, the research results are expected to provide new insights, which could be valuable for decision-makers in various capacities, such as preventive measures, healthcare facility provision, and medication considerations.
References
Agapito, Giuseppe, Milano, Marianna, & Cannataro, Mario. (2022). A python clustering analysis protocol of genes expression data sets. Genes, 13(10), 1839.
Fadhlan Sulistiyo Hidayat1, Rizma Berliana Putri Affandi2, Virgaria Zuliana3, Tesa Nur Padilah4. (2022). Penerapan K-Means Clustering dalam Pengelompokan Kasus Tuberkulosis di Provinsi Jawa Barat Fadhlan. 8(September), 219–227.
Istiharoh, Tri Riskinie, Djannah, Fathul, & Ajmala, Indana Eva. (2022). Hubungan Antara Gambaran Sitologi Menggunakan Metode Fnab Dengan Respons Terapi Pada Pasien Limfadenitis Tuberkulosis Di Pulau Lombok. Jurnal Kedokteran Unram, 11(4), 1169–1175.
Oktaviani, Siska Dewi, Sumarni, Tri, & Supriyanto, Teguh. (2023). Studi Kasus Implementasi Batuk Efektif pada Pasien dengan Tuberkulosis Paru. Jurnal Penelitian Perawat Profesional, 5(2), 875–880.
Palacios, Carlos A., Reyes-Suárez, José A., Bearzotti, Lorena A., Leiva, Víctor, & Marchant, Carolina. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile. Entropy, 23(4), 485.
Shirazy, Adel, Hezarkhani, Ardeshir, Shirazi, Aref, Khakmardan, Shayan, & Rooki, Reza. (2022). K-means clustering and general regression neural network methods for copper mineralization probability in Chahar-Farsakh, Iran. Türkiye Jeoloji Bülteni, 65(1), 79–92.
Syukron, Hamdi, Fayyad, Muhammad Fauzi, Fauzan, Farin Junita, Ikhsani, Yulia, & Gurning, Umairah Rizkya. (2022). Perbandingan K-Means K-Medoids dan Fuzzy C-Means untuk Pengelompokan Data Pelanggan dengan Model LRFM: Comparison K-Means K-Medoids and Fuzzy C-Means for Clustering Customer Data with LRFM Model. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2(2), 76–83.
WHO. (2022). Global Tuberculosis Report.
Yani, Desy Indra, Pebrianti, Riani, & Purnama, Dadang. (2022). Gambaran Kesehatan Lingkungan Rumah pada Pasien Tuberkulosis Paru. Jurnal Keperawatan Silampari, 5(2), 1080–1088. https://doi.org/10.31539/jks.v5i2.3548
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