The Role Of Predictive Analytics In Early Disease Detection: A Data-Driven Approach To Preventive Healthcare

Abstract

The increasing global burden of chronic diseases, which accounts for nearly 70% of healthcare costs, has driven a paradigm shift toward preventive healthcare strategies emphasizing early detection and intervention. Predictive analytics, powered by artificial intelligence (AI) and machine learning (ML), offers transformative potential in identifying the early onset of diseases such as diabetes, cardiovascular conditions, and cancer, which together contribute to over 45% of hospital admissions. This systematic review, based on the analysis of 37 selected studies from an initial pool of 687 articles, explores the current landscape of predictive analytics in healthcare, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Key findings highlight that predictive models leveraging electronic health records, wearable devices, and demographic data have improved diagnostic accuracy by up to 40% compared to traditional methods. Additionally, applications in real-time monitoring, pandemic forecasting, and resource allocation demonstrate the scalability and versatility of predictive analytics across diverse healthcare settings. However, significant challenges persist, including data privacy concerns, biases in model predictions, and integration barriers within existing healthcare infrastructures. The review concludes that interdisciplinary collaboration among healthcare providers, data scientists, and policymakers is essential to address these challenges and unlock the full potential of predictive analytics in advancing preventive healthcare and achieving more equitable and efficient health outcomes globally.