The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Discovering knowledge from low-quality meterological databases
Knowledge discovery and data mining
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Feature selection and classification model construction on type 2 diabetic patient’s data
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
Application of data mining techniques to determine patient satisfaction
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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This paper describes the analysis of a database of diabetic patients' clinical records and death certificates. The objective of the study was to find rules that describe associations between observations made of patients at their first visit to the hospital and early mortality. Pre-processing was carried out and a knowledge discovery in databases (KDD) package, developed by the Lanner Group and the University of East Anglia, was used for rule induction using simulated annealing. The most significant discovered rules describe an association that was not generally known or accepted by the medical community, however, recent independent studies confirm their validity.