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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Leveraging machine learning in healthcare : Exploring benefits and challenges

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pp. 459–467Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1563XML
Published Online:
17 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1563
Pages:
459–467

Abstract

Recent technology advancements have driven a technological shift in the healthcare industry. A vital component of this development is the increasing prominence of machine learning (ML) inside the medical industry’s framework. This study investigates the various uses, advantages, disadvantages, and issues related to machine learning (ML) in the healthcare sector. Personalized treatment plans and illness detection are just two of the many uses of machine learning thoroughly examined in this paper. This paper further explores the difficulties of data collecting, privacy and security concerns, and the effects of machine learning on healthcare efficiency and patient outcomes and future breakthroughs and new technology that could revolutionize healthcare. So that healthcare organizations can better figure out the possible revolutionary implications and challenges of machine learning (ML), this research explores these essential features of ML in the healthcare sector. One of the goals of this research is to create a future where healthcare is better and more patient-centered.

Keywords

Subject Classifications

68M20 [Performance evaluationqueueingand scheduling in the context of computer systems]

References

[1] Alanazi, A., Using machine learning for healthcare challenges and opportunities. Informatics in Medicine Unlocked, 30, p.100924 (2022).
[2] Nayyar, A., Gadhavi, L. and Zaman, N., Machine learning in healthcare: review, opportunities, and challenges. Machine Learning and the Internet of Medical Things in Healthcare, pp.23-45 (2021).
[3] Taneja, H. and Kaur, S., Reputation based novel trust management framework with enhanced availability for cloud. Journal of Parallel and Distributed Computing, 178, pp.43-55 (2023).
[4] Taneja, H. and Kaur, S., Fake feedback detection to enhance trust in cloud using supervised machine learning techniques. In Proceedings of Data Analytics and Management: ICDAM 2021, Volume 2, pp. 789-796 (2022). Springer Singapore.
[5] Saleem, T.J. and Chishti, M.A., Exploring the applications of machine learning in healthcare. International Journal of Sensors Wireless Communications and Control, 10(4), pp. 458-472 (2020).
[6] Qayyum, A., Qadir, J., Bilal, M. and Al-Fuqaha, A., Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering, 14, pp.156-180 (2020).
[7] Guo, Y., Zhao, R., Lai, S., Fan, L., Lei, X. and Karagiannidis, G.K.,  Distributed machine learning for multiuser mobile edge computing systems. IEEE Journal of Selected Topics in Signal Processing, 16(3), pp.460-473 (2022).
[8] Jain, V. and Chatterjee, J.M., Machine learning with health care perspective. Cham: Springer, pp.1-415 (2020).
[9] Mustafa, A. and Rahimi Azghadi, M., Automated machine learning for healthcare and clinical notes analysis. Computers, 10(2), p.24 (2021).
[10] Gupta, S. and Sedamkar, R.R., Machine learning for healthcare: Introduction. Machine learning with health care perspective: Machine learning and healthcare, pp.1-25 (2020).
[11] Rajpoot, N.K., Singh, P. and Pant, B., May. Design and Develop A Delay Sensitive Smart Health Framework Using Nature Inspired Load Balancer. In 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), pp. 427-432 (2023). IEEE.
[12] Rajpoot, N.K., Singh, P. and Pant, B., December. Nature-Inspired Load Balancing Approach in Cloud Computing Environment for Smart Healthcare. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence, pp. 1-6 (2022).
[13] Prasad, M.G., Kumar, D.S., Pratap, M.S., Kiran, J., Chandrappa, S. and Kotiyal, A., June. Enhanced Prediction of Heart Disease Using Machine Learning and Deep Learning. In International Conference on Advanced Communication and Intelligent Systems, pp. 1-12 (2023). Cham: Springer Nature Switzerland.
[14] Prasad, G., Gujjar, P., Kumar, H.N., Kumar, M.A. and Chandrappa, S., Advances of Cyber Security in the Healthcare Domain for Analyzing Data. In Cyber Trafficking, Threat Behavior, and Malicious Activity Monitoring for Healthcare Organizations, pp. 1-14 (2023). IGI Global.
[15] Tripathi, N., Mishra, A.K., Vaqur, M., Sharma, S. and Pandey, N.K.,  July. Improve the Network Security During the Implementation of IoT Application Using Data Encryption and Trustworthy Network. In 2023 World Conference on Communication & Computing (WCONF), pp. 1-6 (2023). IEEE.
[16] Taneja, H. and Kaur, S., An ensemble classification model for fake feedback detection using proposed labeled CloudArmor dataset. Computers & Electrical Engineering, 93, p.107217 (2021).

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