Optimizing Healthcare through Big Data Analytics: A Review of Efficient Algorithm Designs

Abstract

: This paper explores the role of efficient algorithm designs in optimizing healthcare through big data analytics. The paper begins with an introduction to the definition of big data analytics in healthcare and the importance of healthcare optimization through this approach. It then delves into the applications of big data analytics in healthcare, including the use of ML and data mining algorithms for clinical decision-making, healthcare management and administration, and disease surveillance and outbreak prediction. The challenges faced in implementing big data analytics for healthcare are also discussed, including issues related to data quality, privacy, and security, as well as the need for specialized expertise and resources. The paper then focuses on the role of efficient algorithm designs in addressing these challenges, including an overview of algorithm types and characteristics. The paper explores the implications of using efficient algorithm designs and enhance disease surveillance and outbreak prediction. While there are challenges associated with the use of these algorithms, the benefits are too great to ignore, and healthcare providers must continue to explore and adopt new and innovative approaches to optimize healthcare delivery.