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The article investigates the application of association rule mining, in particular the Frequent Pattern Growth (FP-Growth) algorithm, for combinatorial biomarker discovery for Type 2 Diabetes disease. The dataset used for the case study contains measurements from 100 subjects. The results obtained identify “interesting” rules associated with Type 2 Diabetes which were evaluated by the domain expert as being 88,23% relevant and 11,77% partly relevant. A comparative study aiming to compare rule mining methods with other techniques for biomarkers discovery approaches is work in progress.

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Association Rule Mining for Combinatorial Biomarker Discovery. A Case Study on Type 2 Diabetes

  • 会议论文

  • 发表日期：

    2026年

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  • Ioana Ciuciu,
  • Dana Ciobanu,
  • Daniel Murg,
  • Cosmin Lazar

摘要

The article investigates the application of association rule mining, in particular the Frequent Pattern Growth (FP-Growth) algorithm, for combinatorial biomarker discovery for Type 2 Diabetes disease. The dataset used for the case study contains measurements from 100 subjects. The results obtained identify “interesting” rules associated with Type 2 Diabetes which were evaluated by the domain expert as being 88,23% relevant and 11,77% partly relevant. A comparative study aiming to compare rule mining methods with other techniques for biomarkers discovery approaches is work in progress.