Artificial Intelligence and Big Data Analytics for Early Disease Prediction in Healthcare Systems

The increasing burden of late-stage disease diagnosis remains a major challenge for global healthcare systems, despite the growing availability of large-scale clinical and population health data. Delayed detection of chronic and life-threatening conditions continues to drive higher mortality rates, treatment costs, and resource strain, highlighting the need for more proactive and data-driven diagnostic strategies. This study synthesizes recent advancements in artificial intelligence (AI) and big data analytics applied to early disease prediction across diverse healthcare settings. Drawing on peer-reviewed literature, the paper reviews machine learning, deep learning, and hybrid data-driven models used to analyze electronic health records, medical imaging, genomic data, and real-time patient monitoring systems. The findings indicate that AI-driven predictive analytics significantly enhance early detection accuracy, particularly for cardiovascular diseases, diabetes, cancer, and other chronic conditions. Several studies report improved sensitivity, risk stratification, and clinical decision support compared to traditional diagnostic approaches. However, despite these promising outcomes, widespread clinical adoption remains constrained by persistent challenges. These include data quality and bias, limited model generalizability across populations, ethical and regulatory concerns, and difficulties integrating AI tools into existing healthcare workflows. Overall, while AI and big data technologies demonstrate substantial potential to transform early disease prediction, addressing these limitations is critical for achieving reliable, scalable, and equitable real-world deployment.