C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A Knowledge Discovery System with Support for Model Selection and Visualization
Applied Intelligence
Chance discovery in medicine: detection of rare risky events in chronic diseases
New Generation Computing - Special issue: Chance discovery
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Chance Discovery
Detecting Interesting Exceptions from Medical Test Data with Visual Summarization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
Integration of Learning Methods, Medical Literature and Expert Inspection in Medical Data Mining
IEICE - Transactions on Information and Systems
Data mining of multi-categorized data
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
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In this paper, we propose a web-based interactive interface to show medical data analysis results by C4.5, where the physicians can easily confirm or correct medical data and analysed results. It can estimate health levels of medical data where health levels are not labelled, which can be referred to for medical diagnosis support. It demonstrates the possibilities of the chance discovery process, which enables the discovery of hidden or rare but very important relationships (chances) in a medical diagnosis support. We discovered models which are important for determining health levels but cannot be extracted during a machine learning process.