An Empirical Analysis of Big Data Technologies in Predictive Analytics for Healthcare Information Systems
Keywords:
Big Data, Predictive Analytics, Healthcare Information Systems, Hadoop, Spark, NoSQL, Machine Learning, Clinical Decision SupportAbstract
Big Data technologies have transformed the healthcare domain by enabling real-time decision-making and predictive analytics for early detection, diagnosis, and personalized treatment. Predictive analytics leverages massive volumes of heterogeneous data—including clinical, genomic, behavioral, and administrative records—to uncover patterns that support improved healthcare outcomes. This paper examines the empirical role of Big Data tools and techniques in optimizing predictive analytics within healthcare information systems. numerous studies identified the limitations of traditional data-processing systems and highlighted the potential of distributed frameworks such as Hadoop, Spark, NoSQL, and machine learning pipelines. Empirical evidence demonstrates how Big Data approaches enhance scalability, reduce analytical latency, and improve diagnostic accuracy in complex medical environments.
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Copyright (c) 2023 Prince Rakahle P (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
