Literature Review of Predictive Health Analytics Using AI

Abstract

Predictive health analytics, empowered by Artificial Intelligence (AI), has revolutionized healthcare by enhancing disease prediction, diagnosis, and treatment. This literature review delves into the integration of AI in predictive health analytics, exploring various AI techniques, applications, and their impact on healthcare outcomes. The review identifies key themes, including data mining, machine learning (ML) algorithms, Electronic Health Records (EHR), and predictive modeling. Data mining techniques, such as clustering and association rule mining, help extract meaningful patterns and relationships from vast amounts of healthcare data. ML algorithms, including decision trees, random forests, and neural networks, enable the development of predictive models that can forecast disease risk, progression, and treatment outcomes. EHRs provide a comprehensive repository of patient data, facilitating the application of AI in predictive health analytics. Predictive modeling techniques leverage AI algorithms to analyze healthcare data and generate predictions about future health events, enabling proactive interventions and personalized care. This review showcases the potential of AI to revolutionize healthcare by providing personalized, efficient, and accurate predictive analytics, ultimately leading to improved patient outcomes and a more sustainable healthcare system.