Integrating Big Data, ML, and DL for Predictive Analytics in Healthcare: A Comprehensive Analysis and Hybrid Framework Proposal

The paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research gaps, and applications in real world. The study synthesizes findings from recent research papers related to same objective, highlighting the evolution of data-driven technologies in healthcare sector and the challenges which are associated with data integration, privacy, and real-time processing. The big data analytics provides foundational insights from vast healthcare datasets, while ML algorithms enhance predictive accuracy on structured data, while DL models achieve high precision even in complex tasks, such as medical imaging and real-time IoT embedded systems monitoring. This article introduces a novel hybrid approach that combines the strength of all these approaches, employing common data for heterogenous data integration, federated learning for privacy, and a feedback-driven adaptive system for continuous improvement. Analyzing quantitative comparisons, the effectiveness of each approach is illustrated: DL demonstrate high acuracy, and ML, in turn, flexibility with a moderate rate. Discussing the research gaps of current methodologies, the proposed framework discus the scalability, adaptability, and privacy enhancements of this new conceptual model to create more precise and proactive healthcare analytics. Possible future investigations indicate that it is necessary to enhance these adaptive learning solutions and to expand the arbitrary federative learning approaches to treat different interdisciplinary domains.