The Role of Big Data Analytics in Revolutionizing Diabetes Management and Healthcare Decision-Making

The evolving healthcare domain necessitates an upgrade through digitization, integrating patient data, and advanced medical results. In the last couple of decades, advances in information and storage technologies in healthcare have produced vast amounts of data. The remarkable increases in data volumes, along with the enticing prospects and potential inherent in data analysis, have contributed to the concept of Big Data. There is a pressing need within the research community to analyze these large volumes of Big Data. To address this challenge, Big Data Analytics (BDA), the systematic process of examining large and complex datasets to uncover hidden patterns, correlations, and insights for informed decision-making, has emerged. It employs various methodologies and techniques to enable informed decision-making. This study delves into using Machine Learning (ML) in big data environments, explicitly utilizing the MLib library in Apache Spark to derive meaningful insights from diabetic healthcare dataset. The CDC’s Behavioral Risk Factor Surveillance System (BRFSS) was used to empirically demonstrate the advantages of integrating BDA with ML for medical decision-making in Big Data environments. The research finding highlighted the superior performance of Logistic Regression (LR) models compared to other models like Naive Bayes (NB), providing valuable insights for healthcare applications.