<![CDATA[This study aims to analyze chronic disease patient medical records through the implementation of data mining techniques using various classification methods. This study employs a literature review approach with meta-aggregation methods to synthesize evidence from relevant scientific articles. The article selection process involves three stages: duplicate removal, application of inclusion and exclusion criteria, and checking the relevance of the article content to the research objectives. Out of 411 articles found through Google Scholar, 10 articles met the criteria and were used in the analysis. The results show that data mining techniques are effective in identifying patterns and relationships in medical record data, particularly for chronic disease patients. This study also reveals challenges in implementing these techniques, such as the need for high-quality data and accurate interpretation of results. Recommendations include improving data quality, training healthcare workers, and developing more adaptive classification models. This study contributes theoretically and practically to the field of medical records and health information, particularly in the use of data mining to support clinical decision-making. In conclusion, the implementation of data mining techniques can be a valuable tool in improving chronic disease patient management.]]>
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