Leveraging Data Science for Predictive Healthcare: A Systematic Review of Machine Learning Applications
Abstract
The integration of data science in healthcare has significantly improved predictive analytics, enabling early disease detection, personalized treatment plans, and efficient resource management. This paper systematically reviews the latest machine learning models applied in healthcare, including deep learning for medical imaging, natural language processing for clinical text analysis, and ensemble methods for patient risk assessment. The study also explores key challenges such as data quality, model interpretability, and ethical considerations in AI-driven healthcare. By analyzing case studies and real-world applications, this review provides insights into the future of predictive healthcare and suggests pathways for optimizing data-driven medical decision-making.
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References
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