Exploring the Efficacy of Machine Learning Algorithms in Predictive Analytics for Healthcare

Authors

  • Keerti Vyas, Amit Kumar Vyas, Amit Arora

Keywords:

utilization, considerations, reviewing, interpretability

Abstract

Predictive analytics has emerged as a transformative tool in healthcare, leveraging machine learning (ML) algorithms to enhance patient outcomes, optimize resource allocation, and reduce costs. This paper explores the efficacy of various ML algorithms in predictive analytics within the healthcare sector. By reviewing recent studies and applications, we highlight the strengths and limitations of different algorithms and propose a framework for their optimal utilization in predictive healthcare analytics. Our findings indicate that while machine learning offers significant potential, challenges related to data quality, model interpretability, and ethical considerations must be addressed to maximize its benefits in healthcare.

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References

Deo, R. C. (2015). Machine Learning in Medicine. Health and Technology, 5(2), 97-100.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375(13), 1216-1219.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347-1358.

Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

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Published

15.05.2024

How to Cite

Keerti Vyas. (2024). Exploring the Efficacy of Machine Learning Algorithms in Predictive Analytics for Healthcare. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4759 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7002

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

License

Creative Commons License

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