Abstract
ABSTRACT Amidst the escalating global burden of diabetes and its multifaceted complications, there arises an urgent need for robust healthcare information systems tailored to address the complexities of diabetic patient management. This paper introduces a novel statistical assessment healthcare information system engineered to meticulously analyze diabetes-related data by harnessing the power of big data technologies. Diabetes, affecting millions worldwide, exerts substantial strain on healthcare infrastructures, necessitating innovative solutions to optimize patient outcomes and treatment efficacy. Leveraging advancements in data processing and analytics alongside vast medical datasets, this study pioneers a statistical assessment model explicitly crafted for diabetes analysis within a big data paradigm. The model’s efficacy is rigorously evaluated using key performance metrics such as accuracy and F-measure, within the Hadoop framework. Remarkably, the results demonstrate a significant enhancement in performance compared to conventional methodologies, affirming the model’s potential to revolutionize the accuracy and efficiency of diabetes analysis within a big data milieu. Moving forward, further research avenues include refining the model’s algorithms to accommodate diverse data sources and expanding its applicability to encompass a broader spectrum of diabetes-related factors.
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