Optimizing K-means Clustering with Seed Initialization for Osteoporosis Diagnosis Based on Family History
DOI:
https://doi.org/10.35870/ijmsit.v6i1.6648Keywords:
K-means, Seeds, Clustering, Osteoporosis, Rand indexAbstract
World Osteoporosis Day (WOD) is celebrated on October 20 every year, to raise global awareness about the prevention, diagnosis, and treatment of osteoporosis. Urgency in Indonesia, the number of elderly people is projected to reach 71 million people in 2050, which will have an impact on increasing cases of osteoporosis. Therefore, the recommendations based on scientific evidence in this study aim to assist practitioners in preventing osteoporosis in adults and children. This study proposes a method of Improving K-Means Performance through Seeds. The performance of the K-Means clustering algorithm is highly dependent on the random selection of initial centroids, which can lead to unstable clusters, suboptimal local solutions, and increased iterations, particularly in medical datasets such as osteoporosis diagnosis based on family history. Therefore, there is a need for an optimized centroid initialization strategy that can improve clustering accuracy and stability without increasing computational complexity. The dataset used is the osteoporosis dataset as a testing dataset that can be accessed publicly Osteoporosis dataset. The novelty of this study lies in the introduction of Modified Average (MA) approach for centroid initialization, which eliminates random seed dependency and improves clustering stability without increasing computational complexity. From the results of nine experiments with the benchmarking dataset, it can be seen that the method proposed in this study indicates that practically the Proposed method has a tendency to perform better in Rand Index measurement compare to k-means in random seeds.
Downloads
References
Celebi, M. E. (2015). Partitional clustering algorithms. Partitional Clustering Algorithms, September, 1–415. https://doi.org/10.1007/978-3-319-09259-1
Celebi, M. E., & Kingravi, H. A. (2012). Deterministic initialization of the K-means algorithm using hierarchical clustering. International Journal of Pattern Recognition and Artificial Intelligence, 26(7). https://doi.org/10.1142/S0218001412500188
Chen, J., Qi, X., Chen, L., Chen, F., & Cheng, G. (2020). Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowledge-Based Systems, 203, 106167. https://doi.org/10.1016/j.knosys.2020.106167
Daulay, S., & Wandri, R. (2025). Integrating K-Means Clustering and K-Nearest Neighbor Classification for Effective Scholarship Recipient Selection. Sistemasi: Jurnal Sistem Informasi, 14(1), 235-248. https://doi.org/10.32520/stmsi.v14i1.4818
Erisoglu, M., Calis, N., & Sakallioglu, S. (2011). A new algorithm for initial cluster centers in k-means algorithm. Pattern Recognition Letters, 32(14), 1701–1705. https://doi.org/10.1016/j.patrec.2011.07.011
Faran, J., & Aldisa, R. T. (2024). Perbandingan Algoritma K-Means dan K-Medoids Dalam Pengelompokan Kelas Untuk Mahasiswa Baru Program Magister. Journal of Information System Research (JOSH), 5(2), 509–519. https://doi.org/10.47065/josh.v5i2.4753
Farissa, R. A., Mayasari, R., & Umaidah, Y. (2021). Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung. Journal of Applied Informatics and Computing, 5(2), 109–116. https://doi.org/10.30871/jaic.v5i1.3237
Goyal, M., & Kumar, S. (2014). Improving the Initial Centroids of k-means Clustering Algorithm to Generalize its Applicability. Journal of The Institution of Engineers (India): Series B, 95(4), 345–350. https://doi.org/10.1007/s40031-014-0106-z
Indra, I. I., Rizki, U., Jakak, P. M., Prayogi, M. B., & Rahman, M. (2024). Penerapan Metode K-Means Clustering Dalam Pengembangan Strategi Promosi Berbasis Data Penerimaan Mahasiswa Baru (Studi Kasus: Universitas Nurul Huda). Jurnal Nasional Ilmu Komputer, 5(1), 25–43. https://doi.org/10.47747/jurnalnik.v5i1.1656
Kemenkes RI. (2022). Kepmenkes RI no HK.01.07/MENKES/1928/2022 Tentang Pedoman Nasional Pelayanan Kedokteran Tata Laksana Stunting. 1–52.
Laurenso, J., Jiustian, D., Fernando, F., Suhandi, V., & Rochadiani, T. H. (2024). Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia. Journal of Applied Informatics and Computing, 8(1), 62–70. https://doi.org/10.30871/jaic.v8i1.4871
Lu, S., & Braunstein, S. L. (2014). Quantum decision tree classifier. Quantum Information Processing, 13(3), 757–770. https://doi.org/10.1007/s11128-013-0687-5
Mahmuda, F., Sitorus, M. A. R., Widyastuti, H., & Kurniawan, D. E. (2018). Clustering Profil Pengunjung Perpustakaan Menggunakan Algoritma K-Means: (Studi Kasus Perpustakaan BP Batam). Journal of Applied Informatics and Computing, 1(1), 14–21. https://doi.org/10.30871/jaic.v1i1.476
Maulani, V. R., Barata, M. A., & Yuwita, P. E. (2025). A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique. Journal of Applied Informatics and Computing, 9(3), 741–748. https://doi.org/10.30871/jaic.v9i3.9481
Naldi, M. C., & Campello, R. J. G. B. (2014). Evolutionary k-means for distributed data sets. Neurocomputing, 127, 30–42. https://doi.org/10.1016/j.neucom.2013.05.046
R., Lapatta, N. T., Ardiansyah, R., . W., & Angreni, D. S. (2024). Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies. Journal of Applied Informatics and Computing, 8(2), 341–349. https://doi.org/10.30871/jaic.v8i2.8464
Sajidha, S. A., Chodnekar, S. P., & Desikan, K. (2018). Initial seed selection for K-modes clustering – A distance and density based approach. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.04.013
Sitinjak, D. K., Pangestu, B. A., & Sari, B. N. (2022). Clustering Tenaga Kesehatan Berdasarkan Kecamatan di Kabupaten Karawang Menggunakan Algoritma K-Means. Journal of Applied Informatics and Computing, 6(1), 47–54. https://doi.org/10.30871/jaic.v6i1.3855
Tsapanos, N., Tefas, A., Nikolaidis, N., & Pitas, I. (2015). A distributed framework for trimmed Kernel k -Means clustering. Pattern Recognition, 48(8), 2685–2698. https://doi.org/10.1016/j.patcog.2015.02.020
Widyaningrum, R., Sela, E. I., Pulungan, R., & Septiarini, A. (2023). Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection. International Journal of Dentistry, 2023. https://doi.org/10.1155/2023/6662911
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Adiyah Mahiruna, Ngatimin, Rachmat Destriana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
