Review of Machine Learning Models for Healthcare Business Intelligence and Decision Support
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P110Keywords:
Machine Learning, Healthcare Business Intelligence, Decision Support Systems, Clinical Decision Support, Personalized Medicine, Predictive AnalyticsAbstract
The use of machine learning (ML) has probable to revolutionize healthcare, enabling the extraction of meaningful insights from complex and heterogeneous datasets. The capacity of healthcare systems to facilitate evidence-based decision-making is improved through the integration of many data sources, such as medical imaging, electronic health records, patient-generated, genomic profiles, data, and administrative information. Supervised, unsupervised, and reinforcement learning approaches facilitate accurate diagnosis, risk prediction, patient stratification, and personalized treatment recommendations, while ensemble methods improve predictive robustness and reliability. Machine learning applications extend to operational domains, optimizing patient flow, resource allocation, and appointment scheduling, thereby improving efficiency and reducing costs. Clinical decision support systems benefit from adaptive and real-time analytics, enabling timely interventions and improved patient results. The convergence of ML with business intelligence in healthcare allows stakeholders, including clinicians, administrators, and policymakers, to leverage data-driven strategies for both patient-centered care and institutional management. This review synthesizes current methodologies, applications, and emerging opportunities, delivering a complete resource for researchers and practitioners seeking to employ ML in healthcare business intelligence and decision support
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