C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Applied Intelligence
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Data mining in health care applications
Data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality Reduction in Automatic Knowledge Acquisition: A Simple Greedy Search Approach
IEEE Transactions on Knowledge and Data Engineering
Decision-making processes in pattern recognition (ACM monograph series)
Decision-making processes in pattern recognition (ACM monograph series)
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Artificial Intelligence in Medicine
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Feature selection and classification model construction on type 2 diabetic patients' data
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A prescription fraud detection model
Computer Methods and Programs in Biomedicine
Improving the ranking quality of medical image retrieval using a genetic feature selection method
Decision Support Systems
International Journal of Applied Metaheuristic Computing
A new matching strategy for content based image retrieval system
Applied Soft Computing
Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis
Computer Methods and Programs in Biomedicine
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Data mining, through its capacity to discover knowledge embedded in large databases to improve organizational decision-making, has the potential to contribute to efficiencies and cost savings in the increasingly costly healthcare industry. One important aspect of the methods of mining medical databases includes reducing dimensionality through feature selection. Traditionally feature selection is accomplished through stepwise regression, which tends to produce an unnecessarily high number of ''significant'' variables. This paper applies a filter-based feature selection method using inconsistency rate measure and discretization, to a medical claims database to predict the adequacy of duration of antidepressant medication utilization. Compared to traditional stepwise logistic regression, which selected seven variables from a total of nine potential explanatory variables to characterize patients with inadequate antidepressant medication utilization, the filter-based method selected two variables (age and number of claims) to achieve a similar prediction accuracy. This comparison suggests it may be feasible and efficient to apply the filter-based feature selection method to reduce the dimensionality of healthcare databases.