A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A review of feature selection techniques in bioinformatics
Bioinformatics
An efficient SVM-GA feature selection model for large healthcare databases
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Intelligent heart disease prediction system using data mining techniques
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
Artificial Intelligence in Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
Addressing the Future of Clinical Information Systems—Web-Based Multilayer Visualization
IEEE Transactions on Information Technology in Biomedicine
WeAidU-a decision support system for myocardial perfusion images using artificial neural networks
Artificial Intelligence in Medicine
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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Clinical feature selection problem is the task of selecting and identifying a subset of informative clinical features that are useful for promoting accurate clinical diagnosis. This is a significant task of pragmatic value in the clinical settings as each clinical test is associated with a different financial cost, diagnostic value, and risk for obtaining the measurement. Moreover, with continual introduction of new clinical features, the need to repeat the feature selection task can be very time consuming. Therefore to address this issue, we propose a novel feature selection technique for diagnosis of myocardial infarction - one of the leading causes of morbidity and mortality in many high-income countries. This method adopts the conceptual framework of biological continuum, the optimization capability of genetic algorithm for performing feature selection and the classification ability of support vector machine. Together, a network of clinical risk factors, called the biological continuum based etiological network (BCEN), was constructed. Evaluation of the proposed methods was carried out using the cardiovascular heart study (CHS) dataset. Results demonstrate a significant speedup of 4.73-fold can be achieved for the development of MI classification model. The key advantage of this methodology is the provision of a reusable (feature subset) paradigm for efficient development of up-to-date and efficacious clinical classification models.