Risks and Predictors of Non-Alcoholic Liver Disease Progression Using Association Rules Mining

Authors

  • Tasneem A. Gameel Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt
  • Sherine Rady Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt
  • Sanaa Kamal Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt

DOI:

https://doi.org/10.3991/ijoe.v16i06.13629

Keywords:

NAFLD, NASH, Liver Disease, Frequent Pattern Mining, FP Growth Algo-rithm, Serum Fibrosis Markers

Abstract


Non-alcoholic Steatohepatitis Disease (NASH), a progression of Non-alcoholic Fatty Liver Disease (NAFLD), occurs in case of the increase of fat accumulation in the liver. The disease can next progress to fibrosis, cirrhosis or liver cancer. The most accurate way to diagnose NAFLD progression into NASH is through a liver biopsy. This is painful, expensive and difficult to repeat several times to monitor the fibrosis progression. Thus, finding a non-invasive solution through markers can reliably help tracking the disease progression. The objective of this study is to assess the diagnostic and prognostic performance of serum markers to monitor liver disease progression in comparison to findings by liver biopsy. An association rule mining system is proposed using a Frequent Pattern mining algorithm to reach this objective. An Egyptian cohort consisting of 2300 NAFLD and NASH patients is included in an experimental study, where the results showed that the blood tests and serum markers, PIIINP and ELF, can predict the progression of NAFLD into NASH, and can discriminate between the different stages of NASH with confidence value 0.9. The presented results indicate an advantageous promising non-invasive solution in medicine for predicting of the disease and its progression, while avoiding alternative biopsy exposition

Author Biographies

Tasneem A. Gameel, Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt

Ain Shams University, Faculty of Computer and Information Sciences

 

Sherine Rady, Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt

Ain Shams University, Faculty of Computer and Information Sciences

Sanaa Kamal, Ain Shams University, Faculty of Computer and Information Sciences Cairo, Egypt

Ain Shams University, Faculty of Computer and Information Sciences

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Published

2020-05-28

How to Cite

A. Gameel, T., Rady, S., & Kamal, S. (2020). Risks and Predictors of Non-Alcoholic Liver Disease Progression Using Association Rules Mining. International Journal of Online and Biomedical Engineering (iJOE), 16(06), pp. 61–71. https://doi.org/10.3991/ijoe.v16i06.13629

Issue

Vol. 16 No. 06 (2020)

Section

Papers

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