Data Mining in Healthcare: An Overview of Applications, Techniques, and Challenges
Abstract
Artificial intelligence (AI) and the widespread use of electronic health records (EHRs) are driving a data-driven revolution in the healthcare sector mining techniques are extremely useful for deriving significant insights from the large and intricate datasets shaped in healthcare environments. The state-of-the-art uses of data mining in healthcare are examined in this paper, with a focus on AI and machine learning for personalized medicine, risk assessment, disease detection, and therapy optimization. We explore the difficulties that come with data mining for healthcare, including the requirement for interpretable models, data quality, heterogeneity, and privacy data mining and artificial intelligence work together in a way that has the potential to completely transform healthcare delivery, improving patient outcomes, cutting costs, and speeding up medical research. But it's still critical to address ethical issues and make sure data usage is transparent while recognizing the challenges involved, this study emphasizes the revolutionary potential of data mining in healthcare and offers insightful advice for practitioners and scholars in this quickly developing sector.
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