Abstract

The integration of artificial intelligence (AI) and big data analytics has revolutionized predictive healthcare, offering transformative solutions for early diagnosis, treatment optimization, and personalized care. This chapter explores the critical role of AI models and real-time big data processing in shaping the future of healthcare analytics. Key concepts such as predictive healthcare, big data, machine learning, and AI-driven decision-making are discussed in detail, with a focus on advanced methodologies and tools that enable the processing and analysis of vast healthcare datasets. The chapter highlights the potential of hybrid AI models, cloud and edge computing, and data augmentation techniques in addressing the challenges of healthcare data quality, privacy, and scalability. Additionally, it examines the real-time processing capabilities essential for the timely delivery of insights, enhancing decision-making processes across various healthcare applications. The discussion emphasizes the importance of data preprocessing, integration, and transformation in ensuring high-quality and actionable outcomes. With the increasing demand for precision medicine and personalized healthcare, this chapter provides a comprehensive overview of the intersection between AI, big data, and healthcare, offering valuable insights into current trends, challenges, and future opportunities.

Introduction

The healthcare industry was experiencing a paradigm shift with the integration of artificial intelligence (AI) and big data analytics, driving significant improvements in patient care and clinical decision-making [1]. Predictive healthcare analytics leverages vast amounts of data collected from diverse sources such as electronic health records (EHRs), medical imaging, wearable devices, and genomic data [2]. The application of AI, particularly machine learning and deep learning techniques, allows healthcare professionals to make more informed predictions about patient health outcomes, enabling early interventions and personalized treatment plans [3]. As healthcare systems globally struggle with limited resources and rising patient numbers, predictive analytics offers a scalable solution to optimize treatment and improve operational efficiency [4]. In combination with big data processing, are revolutionizing predictive healthcare by providing timely, accurate, and actionable insights for clinicians, researchers, and policymakers [5].

Real-time big data processing plays a pivotal role in enabling predictive healthcare analytics [6]. Traditional methods of data analysis often suffer from delays due to manual processes or the inability to handle large volumes of data in real-time [7]. with advancements in cloud computing, edge computing, and distributed data processing technologies, healthcare providers can now access real-time insights that support immediate clinical decision-making [8]. These technologies enable healthcare systems to analyze vast streams of data from diverse sensors, wearables, and medical devices asare generated, allowing for dynamic and up-to-date predictions of patient health [9]. The ability to process data in real-time not only enhances the accuracy of predictions but also improves the overall responsiveness of healthcare services, contributing to better patient outcomes and more efficient resource allocation [10].