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Ethics code: IR.US.PSYEDU.REC.1404.007
Shiraz University
Abstract: (10 Views)
Introduction: With the widespread adoption of electronic health records, the volume of healthcare data is rapidly increasing. Data mining, as a powerful tool, enables the extraction of valuable knowledge from these data, playing a crucial role in improving disease diagnosis, treatment management, and optimizing healthcare systems.
Methods: This systematic review follows the PRISMA guidelines. A search was conducted in PubMed, Scopus, Science Direct, and Magiran databases using relevant keywords. From 1,461 identified studies, 32 eligible studies were selected after screening. Study quality was assessed using the Joanna Briggs Institute checklist.
Findings: Data mining is widely applied in healthcare, including predicting chronic diseases (e.g., diabetes and cardiovascular diseases), early diagnosis, and healthcare resource management. Algorithms such as decision trees, neural networks, and support vector machines were most commonly used. Integrating data mining with clinical decision support systems has improved diagnostic accuracy and treatment quality.
Conclusion: Data mining is a transformative technology in healthcare with significant potential to enhance services and reduce costs. However, challenges such as data quality, privacy concerns, and the need for specialized expertise must be addressed. Developing international standards and fostering interdisciplinary collaboration are essential for effective utilization of this technology.
Methods: This systematic review follows the PRISMA guidelines. A search was conducted in PubMed, Scopus, Science Direct, and Magiran databases using relevant keywords. From 1,461 identified studies, 32 eligible studies were selected after screening. Study quality was assessed using the Joanna Briggs Institute checklist.
Findings: Data mining is widely applied in healthcare, including predicting chronic diseases (e.g., diabetes and cardiovascular diseases), early diagnosis, and healthcare resource management. Algorithms such as decision trees, neural networks, and support vector machines were most commonly used. Integrating data mining with clinical decision support systems has improved diagnostic accuracy and treatment quality.
Conclusion: Data mining is a transformative technology in healthcare with significant potential to enhance services and reduce costs. However, challenges such as data quality, privacy concerns, and the need for specialized expertise must be addressed. Developing international standards and fostering interdisciplinary collaboration are essential for effective utilization of this technology.
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