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©The Author(s) 2024.
World J Methodol. Jun 20, 2024; 14(2): 92608
Published online Jun 20, 2024. doi: 10.5662/wjm.v14.i2.92608
Published online Jun 20, 2024. doi: 10.5662/wjm.v14.i2.92608
Figure 1 Patient data extraction and management: A flowchart illustrating the process of data extraction and management in the study.
CAD: Coronary artery disease; DM: Diabetes mellitus.
Figure 2 Schematic demonstrating calculation of support, confidence, and lift using clinical data for association rule mining.
CAD: Coronary artery disease; DM: Diabetes mellitus; M: Male; F: Female; UM: Unmarried.
Figure 3 Association rule mining results for patients in the coronary artery disease-with-diabetes group, showing a support of 33%.
CAD: Coronary artery disease; HBA1C: Glycated hemoglobin.
Figure 4 Association rule mining results for patients in the coronary artery disease-without-diabetes group, showing a support of 33%.
CAD: Coronary artery disease; HBA1C: Glycated hemoglobin.
Figure 5 Association rule mining results for subjects in the healthy control group, showing a support of 33%.
HC: Healthy control; CAD: Coronary artery disease; HBA1C: Glycated hemoglobin.
- Citation: Dabla PK, Upreti K, Shrivastav D, Mehta V, Singh D. Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus. World J Methodol 2024; 14(2): 92608
- URL: https://www.wjgnet.com/2222-0682/full/v14/i2/92608.htm
- DOI: https://dx.doi.org/10.5662/wjm.v14.i2.92608