Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
A Dynamic Programming Based Pruning Method for Decision Trees
INFORMS Journal on Computing
Analysis on risk factors for cervical cancer using induction technique
Expert Systems with Applications: An International Journal
Comparing classification techniques for predicting essential hypertension
Expert Systems with Applications: An International Journal
ECM-aware cell-graph mining for bone tissue modeling and classification
Data Mining and Knowledge Discovery
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
Artificial intelligence models to stratify cardiovascular risk in incident hemodialysis patients
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
With this research, we sought to examine the performance of six different regression tree data mining methods to predict mortality in head injury. Using a data set consisting of 1603 head injury cases, we assessed the performance of: the Classification and Regression Trees (CART) method; the Chi-squared Automatic Interaction Detector (CHAID) method; the Exhaustive CHAID (E-CHAID) method; the Quick, Unbiased, Efficient Statistical Tree (QUEST) method; the Random Forest Regression and Classification (RFRC) method; and the Boosted Tree Classifiers and Regression (BTCR) method, in each case based on sensitivity, specificity, positive/negative predictive, and accuracy rates. Next, we compared their areas under the (Receiver Operating Characteristic) ROC curves. Finally, we examined whether they could be grouped in meaningful clusters with hierarchical cluster analysis. Areas under the ROC curves of regression tree data mining methods ranged from 0.801 to 0.954 (p