Association Rule Mining to Identify Clinical Factors Linked to Disease Severity in Liver Cirrhosis

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Published: Dec 27, 2025
Keywords:
Association Rule Mining Liver Cirrhosis Disease Severity Clinical Factors

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Rachasak Somyanonthanakul
Thammasat University

Abstract

This study applies the association rule mining to identify critical clinical patterns linked to different stages of liver cirrhosis. Utilizing data from the Mayo Clinic primary biliary cirrhosis trial (1974-1984), the research analyzed 5,805 patient records after applying inclusion and exclusion criteria. The Apriori algorithm was used to extract association rules, with measures including support, confidence, and lift. The analysis revealed distinct factors associated with each disease stage. For instance, Stage 1 was strongly linked with normal laboratory values like SGOT and Bilirubin. Stage 3 was predominantly associated with abnormal clinical markers, including high prothrombin, low platelets, and age over 60. A dendrogram further clustered these factors, visually reinforcing the associations. The discovered rules provide valuable insights into the combinations of clinical and laboratory features that characterize cirrhosis progression. These findings can aid in early risk stratification and inform clinical decision-making for targeted patient management.

Article Details

How to Cite
[1]
R. Somyanonthanakul, “ Association Rule Mining to Identify Clinical Factors Linked to Disease Severity in Liver Cirrhosis”, JIST, vol. 15, no. 2, pp. 77–84, Dec. 2025.
Issue
Vol. 15 No. 2 (2025): Journal of Information Science and Technology (JIST) [Jul. 2025 - Dec. 2025]
Section
Research Article: Multidisciplinary (Detail in Scope of Journal)

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