USING BIG DATA IN HEALTHCARE: UNLOCKING INSIGHTS TO IMPROVE PUBLIC HEALTH OUTCOMES

Abstract

Availability of large-scale data sources in the healthcare industry presents an unprecedented opportunity to revolutionize the healthcare systems in this regard. This study narrates how big data can play a leading role in its ability to open the gates of insight to better public health outcomes. In particular, it explores the ways in which machine learning, predictive models and data analytics can be used to increase disease prevention, perfect treatment strategies, and manage the resource allocation of healthcare. Its analysis involves the application of multiple diagnostic characteristics to the healthcare datasets held in public health databases, as well as clinical records, using different types of the machine learning methods such as supervised learning, unsupervised learning, and deep learning models. Viable opportunities of predictive models in the field of finding at-risk populations, predicting health trends, and data privacy, integration, and scalability issues are reflected in the findings. The study will help build up the literature in healthcare analytics and offer a suggestion for future studies and the involvement of AI and big data in healthcare policy. The findings outlines the importance of big data in enhancing public health outcomes and provide ways to overcome the present limitations in this aspect. 

Keywords Big Data, Health Care, Predictive Modelling, Public Health, Machine Learning, Data Analytic, Ai