Literature DB >> 17851054

Evaluation of rule interestingness measures in medical knowledge discovery in databases.

Miho Ohsaki1, Hidenao Abe, Shusaku Tsumoto, Hideto Yokoi, Takahira Yamaguchi.   

Abstract

OBJECTIVE: We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. METHODS AND MATERIALS: We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical expert's interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. RESULTS AND
CONCLUSION: The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.

Entities:  

Mesh:

Year:  2007        PMID: 17851054     DOI: 10.1016/j.artmed.2007.07.005

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  4 in total

1.  Using medical history embedded in biometrics medical card for user identity authentication: privacy preserving authentication model by features matching.

Authors:  Simon Fong; Yan Zhuang
Journal:  J Biomed Biotechnol       Date:  2012-04-05

2.  Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data.

Authors:  Jiewu Leng; Dewen Wang; Xin Ma; Pengjiu Yu; Li Wei; Wenge Chen
Journal:  Appl Intell (Dordr)       Date:  2022-02-22       Impact factor: 5.019

3.  Big data analytics for preventive medicine.

Authors:  Muhammad Imran Razzak; Muhammad Imran; Guandong Xu
Journal:  Neural Comput Appl       Date:  2019-03-16       Impact factor: 5.102

4.  Area under precision-recall curves for weighted and unweighted data.

Authors:  Jens Keilwagen; Ivo Grosse; Jan Grau
Journal:  PLoS One       Date:  2014-03-20       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.