The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integration, quality, model interpretability, and clinical relevance. It suggests improvements in terms of hybrid machine learning models, enhanced methods for data preprocessing, and considerations on ethics. In such a way, it is trying to provide a roadmap for future research and practical implementation of predictive analytics in healthcare.
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