Big Data Analytics In Healthcare: Tools, Techniques, And Applications - A Systematic Review

Abstract

Big Data Analytics (BDA) has emerged as a transformative force in healthcare, offering innovative solutions to analyze large and complex datasets for actionable insights. This systematic review, encompassing 142 peer-reviewed studies published between 2010 and 2024, explores the tools, techniques, and applications of BDA in healthcare. The findings reveal the critical role of BDA in enhancing clinical decision-making, optimizing hospital workflows, and advancing medical research. Key applications such as predictive analytics for disease prevention, real-time monitoring through IoT integration, and precision medicine through genomic analysis are highlighted. Tools like Hadoop, Spark, and TensorFlow, combined with advanced techniques such as machine learning and natural language processing, have been pivotal in transforming healthcare data into actionable knowledge. However, the review also identifies significant challenges, including data integration issues, algorithmic bias, and ethical concerns related to patient privacy and data security. By addressing these barriers, BDA has the potential to revolutionize healthcare delivery, providing more personalized, efficient, and equitable care. This study provides a comprehensive understanding of the current state of BDA in healthcare, its limitations, and its promising future applications, offering valuable insights for researchers, policymakers, and healthcare practitioners.