Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Applying Data Mining Techniques to a Health Insurance Information System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
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In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD) The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.