Optimised Cluster-based Approach for Healthcare Data Analytics

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Published Feb 15, 2023
https://doi.org/10.47164/ijngc.v14i1.1011
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Volume 14, Special Issue 1, February 2023

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Amol Bhopale
Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Sanskar Zanwar
Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Aarya Balpande
Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Jaweria Kazi
Shri Ramdeobaba College of Engineering and Management, Nagpur, India

Abstract

Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the healthcare data for effective analysis is complicated and challenging most of the time. This research work examines clustering approaches on the healthcare datasets with the optimum K-clusters, in order to perform the analysis of the data. In this work, the K-means clustering method is examined and the silhouette score is computed to estimate the optimal K-value and the quality of the cluster.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Amol Bhopale, Sanskar Zanwar, Aarya Balpande, & Jaweria Kazi. (2023). Optimised Cluster-based Approach for Healthcare Data Analytics. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1011
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References

  1. Banait, S. S., Sane, S., and Talekar, S. A. 2022. An efficient clustering technique for big data mining. International Journal of Next-Generation Computing 13, 3. DOI: https://doi.org/10.47164/ijngc.v13i3.842
  2. CDC. 2022. Cdc. https://www.cdc.gov/brfss/annual data/annual 2015.html. Accessed: 2022-11-01.
  3. Kriegel, H.-P., Kr¨oger, P., Sander, J., and Zimek, A. 2011. Density-based clustering. Wiley interdisciplinary reviews: data mining and knowledge discovery 1, 3, 231–240. DOI: https://doi.org/10.1002/widm.30
  4. Murtagh, F. and Contreras, P. 2011. Methods of hierarchical clustering. arXiv preprint arXiv:1105.0121.
  5. Nickel, R. M. and Williams, W. 2000. High resolution frequency tracking via non-negative time-frequency distributions. In Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No. 00TH8496). IEEE, 612–615.
  6. Ogbuabor, G. and Ugwoke, F. 2018. Clustering algorithm for a healthcare dataset using silhouette score value. International Journal of Computer Science & Information Technology 102, 2018, 27–37. DOI: https://doi.org/10.5121/ijcsit.2018.10203
  7. Scikit. 2022a. https://scikit-learn.org/stable/.
  8. Scikit. 2022b. Scikit. https://scikit-learn.org/stable/modules/generated/sklearn .preprocessing.KBinsDiscretizer.html. Accessed: 2022-11-01.
  9. Vahiny Sharma, Ankur Gupta, Najam Ul Hasan, Mohammad Shabaz, Isaac Ofori, "Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions", Security and Communication Networks, vol. 2022, Article ID 9697545, 15 pages, 2022. https://doi.org/10.1155/2022/9697545 DOI: https://doi.org/10.1155/2022/9697545
  10. Scikit. 2022c. Scikit. https://scikit-learn.org/stable/auto examples/preprocessing/plot discretization classification.html. Accessed: 2022-11-01.
  11. Scikit. 2022d. Scikit. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouettescore.html?highlight=silhouette#sklearn.metrics.silhouette score. Accessed: 2022-11-01.
  12. Sen, S., Das, M., and Chatterjee, R. 2018. Estimation of incomplete data in mixed dataset. In Progress in intelligent computing techniques: theory, practice, and applications. Springer, 483–492. DOI: https://doi.org/10.1007/978-981-10-3373-5_48
  13. Sokat, K. Y., Dolinskaya, I. S., Smilowitz, K., and Bank, R. 2018. Incomplete information imputation in limited data environments with application to disaster response. European Journal of Operational Research 269, 2, 466–485. DOI: https://doi.org/10.1016/j.ejor.2018.02.016
  14. Swarndeep Saket, J. and Pandya, S. 2016. An overview of partitioning algorithms in clustering techniques. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 5, 6, 1943–1946.