Comparison between objective interestingness measures and real human interest in medical data mining

Miho Ohsaki; Yoshinori Sato; Shinya Kitaguchi; Hideto Yokoi; Takahira Yamaguchi

doi:10.1007/978-3-540-24677-0_110

Comparison between objective interestingness measures and real human interest in medical data mining

Miho Ohsaki, Yoshinori Sato, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi

Research output: Contribution to journal › Conference article › peer-review

11 Citations (Scopus)

Abstract

This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and ² Measure 1 demon-strated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.

Original language	English
Pages (from-to)	1072-1081
Number of pages	10
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	3029
DOIs	https://doi.org/10.1007/978-3-540-24677-0_110
Publication status	Published - 2004
Externally published	Yes
Event	17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada Duration: 2004 May 17 → 2004 May 20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-540-24677-0_110

Cite this

Comparison between objective interestingness measures and real human interest in medical data mining. / Ohsaki, Miho; Sato, Yoshinori; Kitaguchi, Shinya; Yokoi, Hideto; Yamaguchi, Takahira.

In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 3029, 2004, p. 1072-1081.

Research output: Contribution to journal › Conference article › peer-review

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AU - Yamaguchi, Takahira

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AB - This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and 2 Measure 1 demon-strated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.

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Comparison between objective interestingness measures and real human interest in medical data mining

Abstract

ASJC Scopus subject areas

UN SDGs

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