Healthcare Industry and its Enhancement using Machine Learning: An Overview

by K. Sreekumar
Associate Professor, SRM Institute of Science and Technology (SRM University), TN, India

10.46679/978819573220306

Sreekumar, K. (2023). Healthcare Industry and its Enhancement using Machine Learning: An Overview. In P.K. Paul, S. Sharma, E. Roy Krishnan (Eds.), Advances in Business Informatics empowered by AI & Intelligent Systems (pp 87-95). CSMFL Publications. https://dx.doi.org/10.46679/978819573220306

Abstract

This paper presents the effect of AI in the Medical services industry. AI (ML) is a subclass of computerized reasoning innovation, where calculations process huge informational indexes to recognize designs, gain from them, and execute undertakings independently without being told precisely how to resolve the issue. Lately, the wide accessibility of strong equipment and distributed computing has brought about a more extensive reception of ML in various areas of living souls, from involving it for proposals via online entertainment to taking on it for process robotization in plants. Also, its reception will just become further. Medical care is an industry that stays aware of the times also. With how much information is created for every patient, AI calculations in medical services have extraordinary potential.

Keywords: Healthcare, Social media, Automation, Adoption, Modeling, Strategy

This work is a part of: Advances in Business Informatics empowered by AI & Intelligent Systems (Eds. P.K. Paul, Sushil Sharma, Edward Roy Krishnan)

© CSMFL Publications & its authors.

References

  1. Abdelaziz Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117–128. https://doi.org/10.1016/j.measurement.2018.01.022
  2. Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable Machine Learning in Healthcare. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. https://doi.org/10.1145/3233547.3233667
  3. A.Jabbar, M., Samreen, S., & Aluvalu, R. (2018). The Future of Health care: Machine Learning. International Journal of Engineering & Technology, 7(4.6), 23. https://doi.org/10.14419/ijet.v7i4.6.20226
  4. Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020). Identifying Ethical Considerations for Machine Learning Healthcare Applications. The American Journal of Bioethics, 20(11), 7–17. https://doi.org/10.1080/15265161.2020.1819469
  5. Dalal, K. R. (2020, July 1). Analysing the Implementation of Machine Learning in Healthcare. IEEE Xplore. https://doi.org/10.1109/ICESC48915.2020.9156061
  6. Dhillon, A., & Singh, A. (2019). Machine Learning in Healthcare Data Analysis: A Survey. Journal of Biology and Today`s World, 8, 1-10.
  7. Jain, V., & Chatterjee, J. M. (Eds.). (2020). Machine Learning with Health Care Perspective. Learning and Analytics in Intelligent Systems. https://doi.org/10.1007/978-3-030-40850-3
  8. Kaur, P., Sharma, M., & Mittal, M. (2018). Big Data and Machine Learning Based Secure Healthcare Framework. Procedia Computer Science, 132, 1049–1059. https://doi.org/10.1016/j.procs.2018.05.020
  9. Kishor, A., Chakraborty, C., & Jeberson, W. (2020). A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning. International Journal of Interactive Multimedia and Artificial Intelligence, In Press(In Press), 1. https://doi.org/10.9781/ijimai.2020.12.004
  10. Li, Y., Shan, B., Li, B., Liu, X., & Pu, Y. (2021). Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis. Journal of Healthcare Engineering, 2021, 1–11. https://doi.org/10.1155/2021/9739219
  11. Marwan, M., Kartit, A., & Ouahmane, H. (2018). Security Enhancement in Healthcare Cloud using Machine Learning. Procedia Computer Science, 127, 388–397. https://doi.org/10.1016/j.procs.2018.01.136
  12. Mozaffari-Kermani, M., Sur-Kolay, S., Raghunathan, A., & Jha, N. K. (2015). Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare. IEEE Journal of Biomedical and Health Informatics, 19(6), 1893–1905. https://doi.org/10.1109/jbhi.2014.2344095
  13. Mustafa, A., & Rahimi Azghadi, M. (2021). Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers, 10(2), 24. https://doi.org/10.3390/computers10020024
  14. Panesar, A. (2019). Machine Learning and AI for Healthcare. Apress. https://doi.org/10.1007/978-1-4842-3799-1
  15. Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021). Secure and Robust Machine Learning for Healthcare: A Survey. IEEE Reviews in Biomedical Engineering, 14, 156–180. https://doi.org/10.1109/rbme.2020.3013489
  16. Siddique, S., & Chow, J. C. L. (2021). Machine Learning in Healthcare Communication. Encyclopedia, 1(1), 220–239. https://doi.org/10.3390/encyclopedia1010021
  17. Waghade, S. S., & Karandikar, A. M. (2018). A comprehensive study of healthcare fraud detection based on machine learning. International Journal of Applied Engineering Research, 13(6), 4175-4178.
  18. Wiens, J., & Shenoy, E. S. (2017). Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. Clinical Infectious Diseases, 66(1), 149–153. https://doi.org/10.1093/cid/cix731

[email protected]

Follow us @