Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers
DOI:
https://doi.org/10.52436/1.jutif.2025.6.3.4577Keywords:
DBSCAN, OPTICS, Agglomerative Clustering, Silhouette Score, Disease DistributionAbstract
The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.
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with multimorbidity in a large dental school population,” J. Clin. Periodontol., vol. 50, no. 12, pp. 1621–1632, 2023, doi: 10.1111/jcpe.13870.
M. T. Kumbeni, P. A. Apanga, M. W. Chanase, J. N. Alem, and N. Mireku-Gyimah, “The role of the public and private health sectors on factors associated with early essential newborn care practices among institutional deliveries in Ghana,” BMC Health Serv. Res., vol. 21, no. 1, 2021, doi: 10.1186/s12913-021-06665-0.
Kementerian Kesehatan RI, “Profil kesehatan Indonesia Tahun 2023,” 2023, [Online]. Available: https://www.kemkes.go.id/id/profil-kesehatan-indonesia-2023
F. Jaotombo et al., “Machine-learning prediction for hospital length of stay using a French medico-administrative database,” J. Mark. Access Heal. Policy, vol. 11, no. 1, 2022, doi: 10.1080/20016689.2022.2149318.
K. Davagdorj, J. Lee, K. H. Park, and K. H. Ryu, “Cost-sensitive neural network for prediction of hypertension using class imbalance dataset,” in Advances in Intelligent Information Hiding and Multimedia Signal Processing, 2021, pp. 44–51. doi: 10.1007/978-981-33-6757-9_6.
J. Fang et al., “A new particle swarm optimization algorithm for outlier detection: industrial data clustering in wire arc additive manufacturing,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 2, pp. 1244–1257, 2024, doi: 10.1109/tase.2022.3230080.
J. Rustamov, Z. Rustamov, N. Badawi, R. Damseh, F. Jasmi, and N. Zaki, “Benchmarking outlier detection algorithms in healthcare: an analysis across diverse medical datasets,” 2024, doi: 10.21203/rs.3.rs-5242162/v1.
D. P. Sari and N. Sumita, “Achievement cluster of COVID-19 vaccination at the South Bengkulu Health Center using agglomerative hierarchical clustering,” J. Stat. Data Sci., vol. 1, no. 2, pp. 10–16, 2022, doi: 10.33369/jsds.v1i2.24082.
C. Tang, H. Wang, Z. Wang, X. Zeng, H. Yan, and Y. Xiao, “An improved optics clustering algorithm for discovering clusters with uneven densities,” Intell. Data Anal., vol. 25, no. 6, pp. 1453–1471, 2021, doi: 10.3233/ida-205497.
X. Han, C. Armenakis, and M. Jadidi, “Dbscan optimization for improving marine trajectory clustering and anomaly detection,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLIII-B4-2, pp. 455–461, 2020, doi: 10.5194/isprs-archives-xliii-b4-2020-455-202.
X. Zhang and S. Zhou, “Woa-dbscan: application of whale optimization algorithm in dbscan parameter adaption,” IEEE Access, vol. 11, pp. 91861–91878, 2023, doi: 10.1109/access.2023.3307412.
L. Chen, C. Gu, S. Zheng, and Y. Wang, “A method for identifying gross errors in dam monitoring data,” Water, vol. 16, no. 7, p. 978, 2024, doi: 10.3390/w16070978.
S. Li, “An improved dbscan algorithm based on the neighbor similarity and fast nearest neighbor query,” IEEE Access, vol. 8, pp. 47468–47476, 2020, doi: 10.1109/access.2020.2972034.
J. Lin, C. Shang, and Q. Shen, “Towards dynamic fuzzy rule interpolation via density-based rule promotion from interpolated outcomes,” Mathematics, vol. 12, no. 3, p. 402, 2024, doi: 10.3390/math12030402.
S. Kossieris, M. Asgarimehr, and J. Wickert, “Unsupervised machine learning for gnss reflectometry inland water body detection,” Remote Sens., vol. 15, no. 12, p. 3206, 2023, doi: 10.3390/rs15123206.
F. T. T. Lai et al., “Comparing multimorbidity patterns among discharged middle-aged and older inpatients between Hong Kong and Zurich: a hierarchical agglomerative clustering analysis of routine hospital records,” Front. Med., vol. 8, 2021, doi: 10.3389/fmed.2021.651925.
J. Dufek, X. Xiao, and R. R. Murphy, “Best viewpoints for external robots or sensors assisting other robots,” IEEE Trans. Human-Machine Syst., vol. 51, no. 4, pp. 324–334, 2021, doi: 10.1109/thms.2021.3090765.
A. Rashid et al., “Geochemical modeling source provenance, public health exposure, and evaluating potentially harmful elements in groundwater: statistical and human health risk assessment (hhra),” Int. J. Environ. Res. Public Health, vol. 19, no. 11, p. 6472, 2022, doi: 10.3390/ijerph19116472.
R. Vatambeti and V. K. Damera, “Intelligent diagnosis of obstetric diseases using hgs-aoa based extreme learning machine,” Acadlore Trans. AI Mach. Learn., vol. 2, no. 1, pp. 21–32, 2023, doi: 10.56578/ataiml020103.
T. A. Munandar and A. Y. Y. Pratama, “Regional clustering based on types of non-communicable diseases using k-means algorithm,” MATRIK J. Manajemen, Tek. Inform. Dan Rekayasa Komput., vol. 23, no. 2, pp. 285–296, 2024, doi: 10.30812/matrik.v23i2.3352.
E. W. Ambarsari, N. Dwitiyanti, N. Selvia, W. N. Cholifah, and P. D. Mardika, “Comparison approaches of the fuzzy c-means and gaussian mixture model in clustering the welfare of the Indonesian people,” KnE Soc. Sci., 2023, doi: 10.18502/kss.v8i9.13315.
H. Xu, L. Zhang, P. Li, and F. Zhu, “Outlier detection algorithm based on k-nearest neighbors-local outlier factor,” J. Algorithm. Comput. Technol., vol. 16, 2022, doi: 10.1177/17483026221078111.
E. O. Amoako et al., “Examining facilitative services for entry into substance use disorder treatment: a cluster analysis of treatment facilities,” PLoS One, vol. 19, no. 5, p. e0304094, 2024, doi: 10.1371/journal.pone.0304094.
J. Carmona‐Pírez et al., “Multimorbidity clusters in patients with chronic obstructive airway diseases in the epichron cohort,” Sci. Rep., vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-83964-w.
M. Science, E. Division, and S. Arabia, “Bayesian spatial functional data clustering: applications in disease surveillance b,” pp. 1–19, 2019, [Online]. Available: https://doi.org/10.48550/arXiv.2407.12633
M. F. Guagliardo, K. A. Jablonski, J. G. Joseph, and D. C. Goodman, “Do pediatric hospitalizations have a unique geography ?,” vol. 9, pp. 1–9, 2004.
V. N. Vakharia, S. Toescu, A. J. Copp, and D. Thompson, “A topographical analysis of encephalocele locations: generation of a standardised atlas and cluster analysis,” Child’s Nerv. Syst., vol. 39, no. 7, pp. 1911–1920, 2023, doi: 10.1007/s00381-023-05883-7.
M. B. Hossen, “A new statistical approach for the identification of outlier genes in cancer microarray data,” Asian J. Med. Biol. Res., vol. 6, no. 4, pp. 795–801, 2021, doi: 10.3329/ajmbr.v6i4.51248.
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