INTEGRATING BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE FOR EARLY DISEASE PREDICTION AND PREVENTIVE HEALTHCARE STRATEGIES

Abstract

The rapid growth of digital technologies has led to an unprecedented increase in healthcare data, giving rise to the concept of Big Data in medicine. Big Data analytics refers to the use of advanced computational techniques to process, analyze, and interpret large and complex datasets. In healthcare, these datasets include electronic health records, medical imaging, genomic data, wearable device outputs, and population health statistics. This study aims to evaluate the role of Big Data analytics in disease prediction and prevention, focusing on its impact on healthcare quality, clinical decision-making, and public health outcomes. A mixed-methods approach was employed, combining quantitative analysis of predictive models with qualitative insights from healthcare professionals. The findings demonstrate that Big Data significantly enhances early disease detection, improves risk stratification, and supports personalized medicine. Additionally, Big Data analytics contributes to more efficient healthcare systems by optimizing resource allocation and reducing costs. However, challenges such as data privacy, integration complexity, and algorithm bias remain significant barriers. The study concludes that Big Data analytics has the potential to revolutionize healthcare by enabling predictive, preventive, and personalized medical approaches.