Association rule-based feature selection method for Alzheimer's disease diagnosis

Authors:
R. Chaves;J. RamíRez;J. M. GóRriz;C. G. Puntonet
Affiliations:
Dept. Signal Theory, Networking and Communication, ETSIIT, 18071 Granada, University of Granada, Spain;Dept. Signal Theory, Networking and Communication, ETSIIT, 18071 Granada, University of Granada, Spain;Dept. Signal Theory, Networking and Communication, ETSIIT, 18071 Granada, University of Granada, Spain;Dept. Computer's Architecture and Technology, ETSIIT, 18071 Granada, University of Granada, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 17
Cited 5

Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
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
Selecting the right interestingness measure for association patterns

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolving Feature Selection

IEEE Intelligent Systems
Statistical Association Rules and Relevance Feedback: Powerful Allies to Improve the Retrieval of Medical Images

CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
An expert system for supervised classifier design: Application to Alzheimer diagnosis

Expert Systems with Applications: An International Journal
Feature selection for ranking

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A review of feature selection techniques in bioinformatics

Bioinformatics
A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases

Expert Systems with Applications: An International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines

Information Sciences: an International Journal
Computer aided diagnosis of Alzheimer's disease using component based SVM

Applied Soft Computing
Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes

Expert Systems with Applications: An International Journal
Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

Neurocomputing
Using Multi-Modal Semantic Association Rules to fuse keywords and visual features automatically for Web image retrieval

Information Fusion
Analysis of spect brain images using Wilcoxon and relative entropy criteria and quadratic multivariate classifiers for the diagnosis of Alzheimer's disease

IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Mining interesting association rules in medical images

ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications

Integrating discretization and association rule-based classification for Alzheimer's disease diagnosis

Expert Systems with Applications: An International Journal
Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease

Expert Systems with Applications: An International Journal
Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease

Expert Systems with Applications: An International Journal
Mining frequent patterns and association rules using similarities

Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

A fundamental challenge that remains unsolved in the neuroimage field is the small sample size problem. Feature selection and extraction, which are based on a limited training set, are likely to display poor generalization performance on new datasets. To address this challenge, a novel voxel selection method based on association rule (AR) mining is proposed for designing a computer aided diagnosis (CAD) system. The proposed method is tested as a tool for the early diagnosis of Alzheimer's disease (AD). Discriminant brain areas are selected from a single photon emission computed tomography (SPECT) or positron emission tomography (PET) databases by means of an AR mining process. Simultaneously activated brain regions in control subjects that consist of the set of voxels defining the antecedents and consequents of the ARs are selected as input voxels for posterior dimensionality reduction. Feature extraction is defined by a subsequent reduction of the selected voxels using principal component analysis (PCA) or partial least squares (PLS) techniques while classification is performed by a support vector machine (SVM). The proposed method yields an accuracy up to 91.75% (with 89.29% sensitivity and 95.12% specificity) for SPECT and 90% (with 89.33% sensitivity and 90.67% specificity) for PET, thus improving recently developed methods for early diagnosis of AD.