Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Comprehensive data warehouse exploration with qualified association-rule mining
Decision Support Systems
Image histogram thresholding based on multiobjective optimization
Signal Processing
Support vector machines for interval discriminant analysis
Neurocomputing
A SVM-based discretization method with application to associative classification
Expert Systems with Applications: An International Journal
Associative classification of mammograms using weighted rules
Expert Systems with Applications: An International Journal
Data & Knowledge Engineering
Computer aided diagnosis of Alzheimer's disease using component based SVM
Applied Soft Computing
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency
IEEE Transactions on Multimedia
Association rule-based feature selection method for Alzheimer's disease diagnosis
Expert Systems with Applications: An International Journal
Functional brain image classification using association rules defined over discriminant regions
Pattern Recognition Letters
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper shows a computer aided diagnosis (CAD) combining continuous attribute discretization and association rule mining for the early diagnosis of Alzheimer's disease (AD) based on emission computed tomography images. A mask is obtained from the mean control images by an image histogram segmentation. 3D voxels centered in mask coordinates are selected by equal-width binning-based discretization of the mean intensity. These Regions of Interest (ROIs) are then used as input for the Association Rule (AR)-mining using control subject images to fully characterize the normal pattern of the image. Minimum support and confidence are fixed to the maximum values in order to obtain the highest predictive power rules for each discretization level (or combination of levels). Finally, classification is carried out by comparing the number of ARs verified by each subject under test. The proposed system is evaluated using two different databases of single photon emission computed tomography (SPECT) and positron emission tomography (PET) images from the Alzheimer Disease Neuroimaging Initiative (ADNI) yielding an accuracy up to 96.91% (for SPECT) and 92% (for PET), thus outperforming the baseline (the so called continuous AR-based method) and other recently reported CAD methods.