Machine Learning Techniques with Mitigation Concept for Predictive Healthcare Analytics

Artificial intelligence and machine learning are fundamentally reshaping the healthcare landscape by enabling predictive analytics, personalizing patient treatments, and improving clinical outcomes. Despite these theoretical advances, integrating predictive models into real-world clinical workflows remains hampered by challenges related to data privacy, model interpretability, and technical accessibility. This comprehensive review examines state-of-the-art machine learning techniques applied to predictive healthcare, exploring how recent innovations attempt to bridge the gap between algorithmic potential and bedside utility. Finally, the paper discusses critical ethical considerations, failure modes, and practical deployment challenges, providing a roadmap for future research in scalable and trustworthy clinical decision support systems