A Comprehensive Survey on using Segmentation and Density Peaks Clustering (DPC) for Healthcare Data Streams
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12460Keywords:
Clustering, Big data, EHealthcare, Deep Learning - Based SegmentationAbstract
Healthcare systems have recently undergone a significant digital transformation, driven by the rapid growth of the Internet of Medical Things (IoMT) and smart sensing technologies. The sensing technologies generate continuous, high-speed stream of medical information that require real-time analysis and processing. Healthcare data streams are evolving over time. Recently, medical data segmentation and clustering are considered one of the most important techniques used to enhance IoMT reliability, scalability and to support the online medical decisions. Furthermore, these techniques employ bandwidth optimization by reducing the overhead and transmission delay. To date, several surveys have also been proposed in the literature. However, current challenges such as real-time processing and dynamic maintaining of wide variety of medical data streams, which raise the question of developing intelligent and adaptive analytical systems for use in the medical field. Therefore, we conduct a comprehensive survey on the recent advancements in the segmentation and clustering methods for healthcare data streams. This survey examines healthcare data streams, employing clustering and segmentation techniques to improve diagnostic accuracy and enable early disease prediction.
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References
Huang, C., Wang, J., Wang, S., & Zhang, Y. (2023). Internet of medical things: A systematic review. Neurocomputing, 557, 126719.
Kostov, M., & Kaloyanova, K. (2023). Real-time data integration in information systems using stream processing for medical data. Annual of Sofia University St. Kliment Ohridski. Faculty of Mathematics and Informatics, 110, 101-110.
Li, X., Wang, Y., & Li, D. (2019). Medical data stream distribution pattern association rule mining algorithm based on density estimation. IEEE Access, 7, 141319-141329.
Amini, A., Wah, T. Y., & Saboohi, H. (2014). On density-based data streams clustering algorithms: A survey. Journal of Computer Science and Technology, 29(1), 116-141.
Kokate, U., Deshpande, A., Mahalle, P., & Patil, P. (2018). Data stream clustering techniques, applications, and models: comparative analysis and discussion. Big Data and Cognitive Computing, 2(4), 32.
Ghesmoune, M., Lebbah, M., & Azzag, H. (2016). State-of-the-art on clustering data streams. Big Data Analytics, 1(1), 13.
L. Sun, J. Ma, Y. Zhang, and H. Wang. Exploring data mining techniques in medical data streams. In Australasian Database Conference, pages 321–332. Springer, 2016.
S. J. Miah, E. Camilleri, and H. Q. Vu. Big data in healthcare research: a survey study. Journal of Computer Information Systems, 62(3):480–492, 2022.
Saravanakumar, R., Maniraj, S. P., Barshan, A. D., Das, S., Hasan, H., & Alazzam, M. B. (2023, November). Clustering big data for novel health care system. In AIP Conference Proceedings (Vol. 2587, No. 1, p. 050006). AIP Publishing LLC.
Nguyen, H. L., Woon, Y. K., & Ng, W. K. (2015). A survey on data stream clustering and classification. Knowledge and information systems, 45(3), 535-569.
Wang, Y., & Jing, B. (2023). A semi-supervised density peaks clustering algorithm. Statistics and Its Interface, 16(3), 363-377.
Zhang, R., Miao, Z., Tian, Y., & Wang, H. (2022). A novel density peaks clustering algorithm based on Hopkins statistic. Expert Systems with Applications, 201, 116892.
Dang, X. H., Lee, V., Ng, W. K., Ciptadi, A., & Ong, K. L. (2009, April). An EM-based algorithm for clustering data streams in sliding windows. In International conference on database systems for advanced applications (pp. 230-235). Berlin, Heidelberg: Springer Berlin Heidelberg.
Hahsler, M., & Bolaños, M. (2016). Clustering data streams based on shared density between micro-clusters. IEEE transactions on knowledge and data engineering, 28(6), 1449-1461.
Gong, S., Zhang, Y., & Yu, G. (2017). Clustering stream data by exploring the evolution of density mountain. Proceedings of the VLDB Endowment, 11(4), 393-405.
Xu, J., Wang, G., Li, T., Deng, W., & Gou, G. (2017). Fat node leading tree for data stream clustering with density peaks. Knowledge-Based Systems, 120, 99-117.
Al-Shammari, A., Zhou, R., Naseriparsaa, M., & Liu, C. (2019). An effective density-based clustering and dynamic maintenance framework for evolving medical data streams. International journal of medical informatics, 126, 176-186.
Yan, X., Homaifar, A., Sarkar, M., Girma, A., & Tunstel, E. (2021). A clustering-based framework for classifying data streams. arXiv preprint arXiv:2106.11823.
Chen, J., Li, P., Fang, W., Zhou, N., Yin, Y., Zheng, H., ... & Wang, R. (2021). Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type‐2 Fuzzy Sets over Medical Data Stream. Wireless Communications and Mobile Computing, 2021(1), 6662254.
Chen, D., Du, T., Zhou, J., Wu, Y., & Wang, X. (2022). DWDP-stream: a dynamic weight and density peaks clustering algorithm for data stream. International Journal of Computational Intelligence Systems, 15(1), 96.
Abdel-Maksoud, E., Elmogy, M., & Al-Awadi, R. (2015). Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal, 16(1), 71-81.
Malathi, M., & Sinthia, P. (2018). MRI brain tumour segmentation using hybrid clustering and classification by back propagation algorithm. Asian Pacific Journal of Cancer Prevention: APJCP, 19(11), 3257.
Hassan, N. S., Abdulazeez, A. M., Zeebaree, D. Q., & Hasan, D. A. (2021). Medical images breast cancer segmentation based on K-means clustering algorithm: a review. Asian Journal of Research in Computer Science, 9(1), 23-38.
Ramesh, K. K. D., Kumar, G. K., Swapna, K., Datta, D., & Rajest, S. S. (2021). A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health & Technology, 7(27).
Khrissi, L., El Akkad, N., Satori, H., & Satori, K. (2022). Clustering method and sine cosine algorithm for image segmentation. Evolutionary Intelligence, 15(1), 669-682.
Mohammed, Y. M., El Garouani, S., & Jellouli, I. (2023). A survey of methods for brain tumor segmentation-based MRI images. Journal of Computational Design and Engineering, 10(1), 266-293.
Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024). Advances in medical image segmentation: A comprehensive review of traditional, deep learning and hybrid approaches. Bioengineering, 11(10), 1034.
Ding, Y., Li, L., Wang, W., & Yang, Y. (2024). Clustering propagation for universal medical image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3357-3369).
Zhu, Y., Zhang, D., Lin, Y., Feng, Y., & Tang, J. (2025). Merging context clustering with visual state space models for medical image segmentation. IEEE Transactions on Medical Imaging.
Wu, J., Wang, Z., Hong, M., Ji, W., Fu, H., Xu, Y., ... & Jin, Y. (2025). Medical sam adapter: Adapting segment anything model for medical image segmentation. Medical image analysis, 102, 103547.
Kusuma, P. V., & Reddy, S. C. M. (2025). MRI brain tumor segmentation and classification with improved U-Net model. Multimedia Tools and Applications, 84(4), 1671-1696.
Zhang, H., Lu, X., Ma, P., Liu, J., Lian, J., & Ma, Y. (2025). Cluster fusion based cross teaching for semi-supervised medical image segmentation. Neurocomputing, 618, 129147.
kaverinskiy, V., Chaikovsky, I., Mnevets, A., Ryzhenko, T., Bocharov, M., & Malakhov, K. (2025). Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN. Computation, 13(6), 144.
Mardani, K., Maghooli, K., & Farokhi, F. (2025). Segmentation of coronary arteries from X-ray angiographic images using density based spatial clustering of applications with noise (DBSCAN). Biomedical Signal Processing and Control, 101, 107175.
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), 1-37.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.
MacQueen, J. (1967). Multivariate observations. In Proceedings ofthe 5th Berkeley Symposium on Mathematical Statisticsand Probability (Vol. 1, pp. 281-297).
Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. science, 344(6191), 1492-1496.
Guo, L., Qin, W., Cai, Z., & Su, X. (2024). Hybrid clustering algorithm based on improved density peak clustering. Applied Sciences, 14(2), 715.
Berahmand, K., Saberi-Movahed, F., Sheikhpour, R., Li, Y., & Jalili, M. (2025). A comprehensive survey on spectral clustering with graph structure learning. arXiv preprint arXiv:2501.13597.
Xingqiong, W., & Kang, L. (2025). Adaptive density peak clustering based on Delaunay graph. PLoS One, 20(6), e0325161.
Liu, S., He, Y., Yang, X., & Yu, Z. (2025). INSDPC: A density peaks clustering algorithm based on interactive neighbors similarity. AIMS MATHEMATICS, 10(4), 9748-9772.
Wang, Y., Qian, J., Hassan, M., Zhang, X., Zhang, T., Yang, C., ... & Jia, F. (2024). Density peak clustering algorithms: A review on the decade 2014–2023. Expert Systems with Applications, 238, 121860.
Yin, L., Wang, Y., Chen, H., & Deng, W. (2022). An improved density peak clustering algorithm for multi-density data. Sensors, 22(22), 8814.
Huang , M., Zou, J., Zhang, Y., Bhatti, U. A., & Chen, J. (2024). Efficient click-based interactive segmentation for medical image with improved Plain-ViT. IEEE Journal of Biomedical and Health Informatics.
Oskouei, A. G., Abdolmaleki, N., Bouyer, A., Arasteh, B., & Shirini, K. (2025). Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering. Biomedical Signal Processing and Control, 100, 107063 .
Mittal, H., Pandey, A. C., Saraswat, M., Kumar, S., Pal, R., & Modwel, G. (2022). A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets. Multimedia Tools and Applications, 81(24), 35001-35026 .Zhu, Y., Zhang, D., Lin, Y., Feng, Y., & Tang, J. (2025). Merging context clustering with visual state space models for medical image segmentation. IEEE Transactions on Medical Imaging.
Huang, C., Wu, Z., Xi, H., & Zhu, J. (2025). kMaXU: Medical image segmentation U-Net with k-means Mask Transformer and contrastive cluster assignment. Pattern Recognition, 161, 111274 .
Baseer, K. K., Sivakumar, K., Veeraiah, D., Chhabra, G., Lakineni, P. K., Pasha, M. J., ... & Harikrishnan, G. (2024). Healthcare diagnostics with an adaptive deep learning model integrated with the Internet of medical Things (IoMT) for predicting heart disease. Biomedical Signal Processing and Control, 92, 105988 .
Aljohani, A. (2024). Optimizing patient stratification in healthcare: A comparative analysis of clustering algorithms for ehr data. International Journal of Computational Intelligence Systems, 17(1), 173.
Wu, Y., Chen, J., Hu, L., Xu, H., Liang, H., & Wu, J. (2025). OmniFuse: A general modality fusion framework for multi-modality learning on low-quality medical data. Information Fusion, 117, 102890.
Kowsar, I., Rabbani, S. B., Akhter, K. F. B., & Samad, M. D. (2023, December). Deep clustering of electronic health records tabular data for clinical interpretation. In 2023 IEEE International Conference on Telecommunications and Photonics (ICTP) (pp. 01-05). IEEE.
Wang, H., Zhang, J., Shen, Y., Wang, S., Deng, B., & Zhao, W. (2025). Improved density peak clustering with a flexible manifold distance and natural nearest neighbors for network intrusion detection. Scientific Reports, 15(1), 8510.
Lv, Y., Liu, M., & Xiang, Y. (2020). Fast searching density peak clustering algorithm based on shared nearest neighbor and adaptive clustering center. Symmetry, 12(12), 2014.
Bian, Z., Chung, F. L., & Wang, S. (2020). Fuzzy density peaks clustering. IEEE Transactions on Fuzzy Systems, 29(7), 1725-1738.
Wang, Y., Wang, D., Zhang, X., Pang, W., Miao, C., Tan, A. H., & Zhou, Y. (2020). McDPC: Multi-center density peak clustering. Neural Computing and Applications, 32(17), 13465-13478.
Abbas, M., El-Zoghabi, A., & Shoukry, A. (2021). DenMune: Density peak based clustering using mutual nearest neighbors. Pattern Recognition, 109, 107589.
Amagata, D., & Hara, T. (2021, June). Fast density-peaks clustering: multicore-based parallelization approach. In Proceedings of the 2021 International Conference on Management of Data (pp. 49-61).
Guan, J., Li, S., He, X., Zhu, J., & Chen, J. (2021). Fast hierarchical clustering of local density peaks via an association degree transfer method. Neurocomputing, 455, 401-418.
Zhou, S., Peng, B., Xu, W., & Ren, L. (2024). SM-DPC: Clustering by fast search and find of density peaks based on SNN with multi-cluster fusion strategy. IEEE Access, 12, 76413-76431.
Guan, J., Li, S., Zhu, J., He, X., & Chen, J. (2024). Fast main density peak clustering within relevant regions via a robust decision graph. Pattern Recognition, 152, 110458.
Liu, Y., Ding, J., Wang, H., & Du, Y. (2025). A Clustering Algorithm Based on the Detection of Density Peaks and the Interaction Degree Between Clusters. Applied Sciences, 15(7), 3612.
Xingqiong, W., & Kang, L. (2025). Adaptive density peak clustering based on Delaunay graph. PLoS One, 20(6), e0325161.
Peng, M., Wu, Y., Lu, Y., Li, M., Zhang, Y., & Cheung, Y. M. (2025, April). Weighted density for the win: Accurate subspace density clustering. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Ren, C., Li, C., Yu, Y., Yang, W., & Guo, R. (2025). Density peak clustering algorithm based on weighted mutual K-nearest neighbors. Frontiers in Applied Mathematics and Statistics, 11, 1598165.
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Copyright (c) 2026 Ghadeer Qasim Al-jaberi, Ahmed Al-Shammari
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