Machine learning in personalized cancer treatment: Implications for global public health
1 Computer and Information Sciences, Faculty of Engineering, Northumbria University, Newcastle, United Kingdom.
2 Department of Nursing, University of Sunderland United Kingdom.
2 Department of Nursing, University of Sunderland United Kingdom.
3 Department of Radiology, University College Hospital, Ibadan, Nigeria.
4Department of Mathematics Joseph Sarwuan Tarka University, Makurdi.
Review Article
GSC Advanced Research and Reviews, 2024, 21(02), 138–158.
Article DOI: 10.30574/gscarr.2024.21.2.0412
Publication history:
Received on 22 September 2024 ; revised on 03 November 2024; accepted on 05 November 2024
Abstract:
Cancer remains a leading cause of mortality worldwide, posing a significant challenge to global public health. Traditional approaches to cancer treatment often adopt a "one-size-fits-all" strategy, which may not be effective for every patient due to the complex and heterogeneous nature of the disease. Personalized cancer treatment aims to tailor therapeutic interventions to the individual characteristics of each patient, enhancing the efficacy and reducing adverse effects. In recent years, machine learning (ML) has emerged as a powerful tool in personalized cancer care, offering the potential to revolutionize diagnosis, prognosis, and treatment by leveraging vast amounts of biomedical data. This review explores the current landscape of machine learning applications in personalized cancer treatment, including its role in imaging, genomics, and drug discovery. We provide an overview of key machine learning techniques, highlight successful case studies from both developed and developing nations, and examine the challenges and limitations associated with the integration of these technologies into clinical practice. Furthermore, we discuss the implications of machine learning-driven personalized cancer care for global public health, emphasizing its potential to address disparities in access to healthcare and improve outcomes in resource-limited settings. Finally, we offer insights into future research directions and policy considerations that could accelerate the adoption of machine learning in global cancer treatment, fostering a more equitable and effective healthcare ecosystem.'
Keywords:
Machine Learning; Personalized; Cancer Treatment; Implications; Global; Public Health
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
