Machine Learning Techniques in Healthcare Business Intelligence: A Comprehensive Review
DOI:
https://doi.org/10.64137/31078699/IJETET-V1I2P103Keywords:
Healthcare business intelligence, Machine learning, Predictive analytics, Clinical decision support systems, Healthcare data analytics, Supervised learning, Unsupervised learning, Deep learning, Reinforcement learning, Population health management, Operational optimization, Healthcare information systemsAbstract
The rapid growth of healthcare data has intensified the need for advanced analytical approaches to support effective business intelligence and decision-making. This study aims to comprehensively review machine learning techniques applied in healthcare business intelligence and examine their contributions to clinical, operational, and strategic performance. Using a systematic literature review methodology, peer-reviewed journal articles, conference proceedings, and industry reports published in recent years were analyzed and synthesized. The review categorizes machine learning techniques into supervised learning, unsupervised learning, deep learning, and reinforcement learning, highlighting their applications in predictive analytics, clinical decision support, operational optimization, fraud detection, and population health management. Key findings indicate that machine learning significantly enhances predictive accuracy, resource efficiency, and data-driven decision-making in healthcare organizations, although challenges related to data quality, privacy, interpretability, and system integration persist. The study concludes that while machine learning has transformative potential for healthcare business intelligence, successful implementation requires robust data governance frameworks, explainable models, and close collaboration between technical experts and healthcare stakeholders.
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