Ensemble Learning in Clinical Decision Support Systems: A Comprehensive Review of Approaches for Symptom Analysis and Real-Time Decision-Making

Abstract

Ensemble learning has become one of the most promising methods for clinical decision support as artificial intelligence is increasingly used in the medical industry, especially in symptom analysis and real-time decision-making. With an emphasis on model types, performance accuracy, interpretability, and their integration with medical sensors and smart devices, this paper offers a thorough overview of ensemble techniques used in Clinical Decision Support Systems (CDSS). Current issues with data privacy, technological constraints, and practicality in clinical settings are also highlighted in the research. It also provides a forward-looking viewpoint on how to improve these systems to deliver healthcare that is more precise, effective, and individualized.